AI Agents for DevOps: Autonomous Incident Response and On-Call Automation in 2026

Posted on November 4, 2026 by Filip Keser

“AI agent” has become the default label for almost any automation feature in incident management tooling, which makes it worth being precise about what each named product actually does, versus what it claims. This roundup covers five platforms — PagerDuty, incident.io, Resolve.ai, Xurrent IMR (the product formerly known as Zenduty), and BigPanda — based on what their own sites, documentation, and press releases say as of November 2026. Every performance figure below is a vendor claim or customer testimonial unless otherwise noted; none are independently benchmarked.

At a Glance

Platform	Named AI Agent(s)	Core Capability	Status
PagerDuty	SRE Agent, Scribe Agent, Shift Agent, Insights Agent	Context gathering, diagnostics, remediation, call transcription, scheduling conflict resolution	SRE/Insights Agent: early access (Oct 2025); Scribe/Shift: generally available
incident.io	Investigations (powered by “Nexus”)	Agentic root-cause hypothesis generation, cross-signal reasoning, PR-only remediation	Generally available (per product page)
Resolve.ai	On-Call Agent (“AI SRE”)	Autonomous alert investigation before a human is paged, severity/blast-radius assessment	Generally available, with guardrails for autonomous actions
Xurrent IMR (formerly Zenduty)	Sera (AI SRE teammate), AI Querier, AI Postmortem Generator	Incident investigation, Q&A, automated postmortem drafting, alert correlation	Generally available
BigPanda	L1 Agent, AI Incident Assistant, AI Detection and Response	Alert correlation, L1 triage, surfacing runbooks/root-cause context	Generally available

PagerDuty: An End-to-End Agent Suite

PagerDuty’s October 2025 “Fall ’25” release introduced what it called an end-to-end AI Agent Suite. The SRE Agent learns from related past incidents, automatically surfaces context, recommends and executes diagnostics and remediations, and generates self-updating runbooks — it was in early access at launch, with general availability projected for Q4 2025. Three other named agents round out the suite: Scribe Agent transcribes Zoom calls and chats and generates status updates in Slack or Microsoft Teams (generally available); Shift Agent automatically detects and resolves on-call scheduling conflicts (generally available); and Insights Agent gives context-aware answers from PagerDuty analytics (early access at launch). PagerDuty’s own release states that early adopters reported “up to a double-digit percentage” faster resolution times, and separately claims customers who tried the broader suite resolved incidents “up to 50% faster” — both are vendor-stated figures without published methodology, and the 50% figure applies to the suite overall rather than the SRE Agent specifically.

incident.io: From Human-Assist to Agentic Investigation

incident.io’s approach has visibly evolved. Its original 2024 AI features — an Assistant for natural-language queries over incident history, suggested incident summaries, related-incident matching, and suggested follow-ups — were explicitly framed as “exoskeletons, not robots,” assisting human responders rather than acting autonomously. Its newer Investigations feature, powered by what incident.io calls “Nexus,” goes further: when an incident is declared, it runs an automatic first-pass analysis, producing root-cause hypotheses with confidence levels, a map of affected services, and suggested next steps, then keeps re-assessing as new signals arrive. An adversarial agent reportedly challenges each hypothesis before it reaches the responder, and every hypothesis links back to its underlying sources. Notably, incident.io states Investigations makes no changes on its own in production — the only action it can take is opening a pull request for a human to review and merge. The vendor’s headline claim is getting teams “an order of magnitude faster” from alert to resolution, with no published baseline; a customer (turbopuffer’s CTO) is quoted saying it did in minutes what would otherwise take a human 30–60 minutes — a single testimonial, not a controlled benchmark.

Resolve.ai: An Agent That Investigates Before You’re Paged

Resolve.ai positions its product as an “always-on AI SRE” that joins the on-call rotation itself. Its On-Call Agent autonomously investigates alerts pulled from PagerDuty and other alerting tools, assesses severity and blast radius with supporting evidence, silences noisy alerts, can run GitHub Actions, and routes findings to the right team — ideally before a human is paged at all. Findings are delivered via Slack, Microsoft Teams, a CLI, or the Resolve.ai interface itself. The vendor describes guardrails distinguishing actions agents can take autonomously from those requiring human approval, and says engineer corrections to agent findings are retained as reusable knowledge. Resolve.ai integrates with Datadog, Grafana, Prometheus, and OpenTelemetry-based observability stacks. Customer-stated outcomes include DoorDash’s Shahrooz Ansari saying investigations moved “from hours to minutes” in many incidents, and Coinbase’s Angelo Marletta citing root causes surfaced “72% faster” than their own teams achieved — both single-customer testimonials rather than independently audited metrics, and Resolve.ai does not publish a standardized time-to-resolution benchmark of its own.

Xurrent IMR (Formerly Zenduty): A Notable Industry Consolidation

Worth flagging on its own: Xurrent acquired Zenduty in February 2025, and Zenduty has since been rebranded as Xurrent IMR (Incident Management and Response). The combined product’s named AI features include Sera, described as an “AI SRE teammate” that reads alert payloads, finds similar past incidents, and pulls related postmortems and Slack threads when triggered from an incident channel; an AI Querier for natural-language questions about impacted services; an AI Postmortem Generator that drafts postmortems from logs, metrics, and chat history against a chosen template; an AI Scheduling Assistant; and AI-powered alert correlation. On outcomes, Xurrent cites specific customer figures rather than only generic claims: Pima Community College reports a 49% reduction in MTTR and alerted 34,000 users during outages; PopClub reports critical alerts reaching the service owner in under a minute with P0 acknowledgment in 10–15 minutes; and Bajaj Finserv Health reports monthly reporting time cut from three hours to zero, with incidents acknowledged in under five minutes. These are customer-attributed case-study figures published by the vendor, not independently verified.

BigPanda: Correlation-First AIOps

BigPanda’s framing is closer to classic AIOps than to an autonomous agent acting on infrastructure. Its platform applies AI-driven correlation across observability data, service desk activity, and external dependency data to group related alerts and cut noise, supported by what it calls an “IT Knowledge Graph” unifying observability, ITSM, and organizational data for root-cause analysis. Named AI components include an L1 Agent that analyzes and assigns incidents using AI reasoning for Level-1 triage workflows, an AI Incident Assistant covering natural-language investigation and automated incident-channel collaboration, and AI Detection and Response, which gathers triage insights from service desk activity, change data, and observability sources. Unlike Resolve.ai or PagerDuty’s SRE Agent, BigPanda’s own product page doesn’t describe its agents directly executing remediations — response is framed as surfacing runbooks and historical resolutions and escalating when necessary. BigPanda’s page gives no quantified MTTR or alert-noise-reduction figures; customer quotes are qualitative (IHG cites “much faster” detection time; NYSE describes no longer manually combing through thousands of alerts), with the $250 billion figure on the page describing industry-wide manual IT operations spend, not a BigPanda-specific result.

Trade-offs at a Glance

Platform	Autonomy Level	Human-in-the-Loop Control	Primary Integration Focus
PagerDuty	Executes diagnostics/remediations; GA status still rolling out per agent	Guardrails per agent; human review of runbooks	Native PagerDuty ecosystem, Slack/Teams, MCP server
incident.io	Investigates and hypothesizes; cannot change production directly	Only output is a PR a human must merge	Code, telemetry, deployment history, MCP-connected tools
Resolve.ai	Investigates proactively, before paging a human; can act within defined guardrails	Configurable autonomous-vs-approval-required actions	Datadog, Grafana, Prometheus, OpenTelemetry, PagerDuty
Xurrent IMR	Investigates, drafts postmortems, correlates alerts	Human triggers/reviews most agent outputs	Slack/Teams, service desk, observability sources
BigPanda	Correlates and triages; surfaces (doesn’t execute) remediation	Human executes fixes; agent escalates when needed	CMDB, ITSM, observability, topology data

Bottom Line

Every platform here uses the word “agent,” but they sit at different points on the autonomy spectrum. PagerDuty and Xurrent IMR lean toward broad agent suites covering investigation, communication, and scheduling. incident.io and Resolve.ai both emphasize investigation-first agents that stop short of touching production directly — incident.io by limiting itself to pull requests, Resolve.ai through configurable guardrails. BigPanda remains closer to traditional correlation-and-triage AIOps than to an agent that acts independently. None of the quantified outcome claims in this space — PagerDuty’s “50% faster,” Resolve.ai’s “72% faster,” Xurrent’s “49% MTTR reduction” — come from independent, cross-vendor benchmarks; they’re vendor-reported or single-customer figures, and prospective buyers should ask each vendor for methodology and a trial against their own incident history before trusting the headline number.

FAQ

Can any of these AI agents actually fix a production incident on their own, with no human involved?
Based on each vendor’s own description, no — not fully autonomously in production. incident.io’s Investigations is explicit that its only direct action is opening a pull request for human review. Resolve.ai and PagerDuty’s SRE Agent can execute some diagnostics/remediations, but both describe configurable guardrails requiring human approval for higher-risk actions.

Is Zenduty still a separate product?
No. Xurrent acquired Zenduty in February 2025, and the product has been rebranded as Xurrent IMR (Incident Management and Response); some of Xurrent’s own case studies still refer to the product as “Zenduty (now Xurrent IMR).”

Which platform has the most mature AI feature set as of late 2026?
PagerDuty has the broadest named agent suite (four distinct agents covering diagnostics, transcription, scheduling, and analytics), though several components were still in early access as of its October 2025 announcement. incident.io’s Investigations and Resolve.ai’s On-Call Agent are both narrower but more deeply agentic in their investigation workflow specifically.

Are the vendor-reported speed improvements (e.g., “50% faster,” “72% faster”) independently verified?
No. Every such figure found across these five vendors’ own sites is either a vendor-reported aggregate without published methodology or a single customer testimonial — none point to a third-party or cross-vendor benchmark.

Do these tools replace PagerDuty-style alerting, or sit on top of it?
They mostly sit on top of or alongside existing alerting. Resolve.ai explicitly ingests alerts “from PagerDuty and other alerting tools” rather than replacing them, and BigPanda’s correlation layer is designed to reduce noise from existing monitoring and ITSM tools rather than replace them outright.

Artificial Intelligence

Best AI Agent Frameworks for Developers in 2026: LangChain vs AutoGen vs CrewAI

Posted on November 1, 2026 by Filip Keser

“AI agent” products for end users — the kind that autocomplete your code or summarize your inbox — are only half the story. Underneath most of them sits an open-source framework that developers use to wire large language models into autonomous, multi-step workflows. Three names dominate that layer in 2026: LangChain (with its agent-orchestration companion LangGraph), Microsoft’s AutoGen, and CrewAI. They solve the same basic problem — getting one or more LLM-driven “agents” to plan, call tools, and hand off work — but they take meaningfully different approaches, and one of the three has just been formally retired in favor of a successor. Here’s what each actually offers, verified against their own repositories and pricing pages.

At a Glance

Framework	License	Development Status (2026)	Core Model	Paid Tier
LangChain / LangGraph	MIT (open source)	Actively developed	Chains and graphs of LLM calls, tools, and agents	LangSmith: free Developer plan, Plus $39/seat/mo + usage, custom Enterprise
AutoGen (Microsoft)	MIT (open source)	Maintenance mode — bug/security fixes only	Multi-agent conversation (agents “chat” to solve tasks)	None; community-managed. Successor: Microsoft Agent Framework
CrewAI	MIT (open source)	Actively developed	Role-based “crews” of agents with defined tasks	Free Cloud tier (50 executions/mo); custom Enterprise

Short Verdict

If you want the biggest ecosystem, the most integrations, and don’t mind a steeper learning curve, LangChain (via LangGraph for the agent-orchestration layer) is the default choice. If you want agents that behave like a structured team with clear roles and tasks, CrewAI is the more opinionated, often faster-to-prototype option. AutoGen is the one to be careful with: Microsoft’s own repository now states it is in maintenance mode and points new projects to Microsoft Agent Framework instead, so it’s a poor choice for anything you plan to build on for the next few years.

What Each One Is

LangChain describes itself, in its own GitHub README, as “a framework for building agents and LLM-powered applications.” It’s the oldest and most sprawling of the three, with hundreds of integrations for models, vector stores, and tools. LangGraph, a companion library, is what most teams now use specifically for building controllable, stateful agents on top of LangChain’s primitives. The company behind both also sells LangSmith, a separate paid observability and deployment platform — the open-source framework itself remains free and MIT-licensed.

AutoGen is Microsoft’s framework for “creating multi-agent AI applications that can act autonomously or work alongside humans,” per its own README. As of 2026, Microsoft’s repository carries an explicit notice: “AutoGen is now in maintenance mode.” It will not receive new features, remains under the microsoft GitHub organization, and is described as “community managed going forward,” with contributions limited to bug fixes, security patches, and documentation. Microsoft directs new users to a separate project, Microsoft Agent Framework, which it calls the “enterprise-ready successor to AutoGen,” along with a migration guide for existing AutoGen users.

CrewAI bills itself as a “Framework for orchestrating role-playing, autonomous AI agents.” Rather than modeling agents as a graph of arbitrary steps, CrewAI organizes work around a “crew”: a set of agents, each with a role, goal, and backstory, assigned to specific tasks. Its own FAQ describes it as a “standalone Python framework with its own primitives” rather than a layer built on top of LangChain.

A Minimal Example

CrewAI’s role-based model is easiest to show in a few lines. This illustrates the shape of the API, not a guaranteed-working script — check current docs for exact argument names before running it:

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Researcher",
    goal="Find and summarize recent facts on a topic",
    backstory="An analyst who double-checks every claim."
)

task = Task(
    description="Research current AI agent framework adoption trends",
    agent=researcher,
    expected_output="A short bulleted summary"
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()

Pros and Cons

LangChain / LangGraph

Pros: Largest integration ecosystem of the three; LangGraph gives fine-grained control over agent state and branching; active, well-funded development; optional LangSmith adds tracing and evaluation.
Cons: Steeper learning curve and more abstractions to learn; LangSmith’s usage-based pricing (billed in “LangChain Standard Units”) can be hard to estimate in advance; some teams find the API surface large relative to what a simple agent needs.

AutoGen

Pros: Still free, open-source (MIT), and usable for existing projects or learning the underlying conversation-pattern concepts; backed by a large existing codebase and community of past adopters.
Cons: Officially in maintenance mode with no new features; Microsoft is actively steering new and existing users toward Microsoft Agent Framework; a risky foundation for a new, long-lived project in 2026.

CrewAI

Pros: Role-based model maps intuitively onto real team workflows; standalone design with its own primitives rather than a wrapper on another framework; free Cloud tier for small workloads; actively developed.
Cons: Less infrastructure-agnostic tooling (tracing, evaluation) than the LangChain ecosystem out of the box; Enterprise pricing isn’t public, so cost at scale requires a sales conversation.

Who It’s a Good Fit For

Teams that need maximum flexibility, lots of third-party integrations, and are willing to invest in learning a bigger framework should default to LangChain/LangGraph. Teams that think of their problem as “a small team of specialists handing off tasks” will likely prototype faster in CrewAI. Teams currently running AutoGen in production should start planning a migration to Microsoft Agent Framework, since Microsoft’s own repository says plainly that AutoGen won’t gain new features going forward.

Who Should Look Elsewhere

Anyone starting a brand-new multi-agent project in 2026 should not pick AutoGen as the long-term foundation, given its maintenance-mode status. Teams wanting a fully managed, no-code agent builder rather than a Python framework should also look elsewhere — all three of these are developer-facing libraries, not low-code platforms.

Bottom Line

All three frameworks are free and MIT-licensed at their core, so the real decision is architecture and longevity, not price. LangChain/LangGraph is the broad, extensible default; CrewAI is the faster, more structured option for role-based workflows; AutoGen is a known quantity that Microsoft itself is winding down in favor of Microsoft Agent Framework. For a new project, weigh LangGraph against CrewAI based on how your problem is shaped, and treat AutoGen as legacy.

FAQ

Is AutoGen dead?

Not dead, but no longer actively developed by Microsoft. Its own repository states it’s in maintenance mode, limited to bug fixes, security patches, and documentation, with Microsoft Agent Framework positioned as its successor.

Is CrewAI built on top of LangChain?

No. CrewAI’s own FAQ describes it as a standalone Python framework with its own primitives, independent of LangChain.

Do I have to pay to use any of these?

No — the core frameworks (LangChain, LangGraph, AutoGen, and CrewAI) are all free and open source under the MIT license. Paid tiers only apply to optional hosted add-ons: LangSmith for LangChain/LangGraph, and CrewAI’s Enterprise/Cloud offering.

Which one is easiest to learn first?

Most developers find CrewAI’s role/task/crew model quicker to grasp for a first project, while LangGraph’s graph-based state model takes longer to learn but scales to more complex, custom agent behavior.

Sources

software, workflow

Zapier vs Make vs n8n: Best Workflow Automation Platform in 2026

Posted on November 1, 2026 by Filip Keser

Workflow automation tools let you connect apps so data moves between them without manual copy-pasting. Zapier popularized the category, Make (formerly Integromat) built a more visual, branching alternative, and n8n took a different route entirely: open-source and self-hostable, with a cloud version for anyone who’d rather not run their own server. Picking between them comes down to how you’re billed for usage, how much visual control you want over the automation logic, and whether you ever want to self-host.

At a Glance

Tool	Free plan	Billing unit	Entry paid plan	Self-hostable
Zapier	100 tasks/month, 2-step Zaps only	Tasks (one per action executed)	Professional, from $19.99/mo	No
Make	1,000 operations/month	Operations/credits (most actions = 1 credit)	Core (exact USD price not published on Make’s own pricing page at time of review)	No
n8n	Community Edition, no published usage cap, self-hosted only	Workflow executions (cloud) or free (self-hosted)	Starter cloud, €20/mo	Yes (Community Edition, via GitHub)

Short Verdict

Zapier is the easiest to start with and has the deepest library of app integrations, but its per-task billing gets expensive fast once a workflow runs often. Make is cheaper per action for complex, branching automations and gives you a visual canvas that shows the whole flow at once, though its published pricing page is less transparent about exact dollar costs. n8n is the pick if you want to self-host for free, need on-premise data control, or want to write custom JavaScript/Python logic directly inside a node — its cloud plans are also the most affordable of the three on a pure price basis, but the richest developer features (custom nodes, tracing) are self-hosted only.

What Each Tool Is

Zapier connects over 7,000 apps through pre-built “Zaps”: a trigger in one app sets off one or more actions in others. It’s built for speed of setup rather than visual complexity — most Zaps are a simple top-to-bottom list of steps.

Make uses a node-based visual canvas where you drag modules onto a board and connect them with lines, including branches, loops, and routers that Zapier’s linear builder doesn’t handle as naturally. It bills in “operations” (credits) rather than flat tasks.

n8n is open-source automation software you can self-host for free (the Community Edition, distributed on GitHub) or run on n8n’s own cloud. It supports writing custom JavaScript or Python directly inside a workflow node, which makes it the most developer-oriented of the three.

Pricing Breakdown

Zapier: Free (100 tasks/mo, 1 user, two-step Zaps only); Professional from $19.99/mo with task tiers up to 2 million/month, multi-step Zaps, premium apps, and webhooks; Team from $69/mo for up to 25 users with a shared workspace and SSO; Enterprise is custom-priced with unlimited users. Annual billing saves roughly a third off the monthly rate.
Make: Free gives 1,000 operations/month, 1 MB of data storage, and 1 data store. Paid tiers are named Core, Pro, Teams, and Enterprise and are billed by operations purchased per month, with extra credits available as auto-top-ups on Core/Pro/Teams. Make’s own pricing page does not display a flat USD figure per tier without selecting an operations volume first, so treat any specific dollar number you see elsewhere as an estimate tied to a particular credit tier rather than a fixed plan price.
n8n: Community Edition is free and self-hosted, with no published execution cap, but you run and maintain the server yourself. Cloud Starter is €20/mo for 2,500 executions/month with a 5-minute max execution time; Cloud Pro is €50/mo for 10,000 executions/month with a 40-minute max execution time and more Assistant AI credits. Business (€667/mo) is self-hosted only at time of review, with the cloud version on a waitlist. Enterprise is custom, available on cloud or self-hosted.

Pros and Cons

Zapier

✅ Largest app library and fastest learning curve
✅ Built-in AI Copilot and AI troubleshooting on every paid plan
❌ Task-based billing scales poorly for high-volume or frequently-polling workflows
❌ No self-hosting option

Make

✅ Visual canvas handles branching, loops, and routers more naturally than a linear builder
✅ Operations-based billing can be cheaper than Zapier’s task billing for multi-step scenarios
❌ Pricing page doesn’t show plain USD figures without picking a credit volume first
❌ No self-hosting option

n8n

✅ Free, self-hosted Community Edition with no vendor lock-in
✅ Native support for custom JavaScript/Python code inside workflow nodes
❌ Richest features (custom nodes, LangSmith/OpenTelemetry tracing, bash scripts) are self-hosted only, not on the cloud plans
❌ Self-hosting means you own the uptime, updates, and security patching

Who It’s a Good Fit For

Choose Zapier if you want the fastest setup, need a long-tail app integration that only Zapier supports, and don’t mind paying a premium for that convenience.

Choose Make if your automations branch, loop, or need a visual map of the whole process, and you’re comfortable working out costs from an operations-based model rather than a flat monthly fee.

Choose n8n if you have the technical capacity to self-host (or don’t mind the cloud plan’s self-hosted-only advanced features), want to write custom code inside your automations, or need to keep workflow data on your own infrastructure for compliance reasons.

Who Should Look Elsewhere

If you need guaranteed flat monthly pricing with no credit or task math at all, all three platforms require some comfort with usage-based billing — budget-sensitive teams should run a test workflow on each free tier before committing to a paid plan, since real-world costs depend heavily on how many steps each automation runs and how often it triggers.

Bottom Line

There’s no single winner here — Zapier wins on integration breadth and ease of use, Make wins on visual control for complex logic, and n8n wins on cost control and openness for anyone willing to self-host. Start with each tool’s free tier on your actual use case before paying for any of them; the billing models are different enough that a workflow cheap on one platform can be expensive on another.

FAQ

Is n8n really free?
The self-hosted Community Edition is free, but you pay for the server it runs on and the time to maintain it. n8n’s own cloud plans start at €20/month and are not free.

Which tool has the most app integrations?
Zapier has the largest published app library by a wide margin, which matters most if you need a niche or less common app connected.

Can I switch between these platforms later?
Not automatically — each platform uses its own workflow format, so moving from one to another means manually rebuilding automations rather than importing them directly.

Do any of these support AI features?
Yes. Zapier includes an AI Copilot and AI-assisted troubleshooting on paid plans; n8n’s cloud plans include monthly AI Assistant credits (self-hosted use relies on your own API key); Make also markets AI agent capabilities within its automation scenarios.

Sources

Cloud & Infrastructure, Engineering

Secure CI/CD Pipelines: A Practical Checklist for Supply-Chain Security in 2026

Posted on October 29, 2026 by Filip Keser

Supply-chain attacks target the pipeline, not just the application — a compromised dependency, a stolen deploy token, or a tampered build step can ship malicious code without ever touching the source repository a reviewer actually read. Three frameworks give teams a shared, non-vendor-specific vocabulary for defending against this: OWASP’s Top 10 CI/CD Security Risks, the SLSA (Supply-chain Levels for Software Artifacts) specification, and NIST’s Secure Software Development Framework (SP 800-218, SSDF). This checklist translates them into concrete controls you can actually implement.

At a Glance

Risk Category	Core Control	Why It Matters
Dependency / supply-chain abuse	Pin dependencies to exact versions or hashes; generate an SBOM	Unpinned or unverified dependencies let attackers slip malicious code in through a routine update (OWASP CICD-SEC-3)
Pipeline execution hijacking	Treat pipeline config files (and anything they can trigger) as code requiring review; restrict what forked-PR workflows can run	Attacker-controlled pipeline definitions can run arbitrary commands with the pipeline’s privileges (OWASP CICD-SEC-4, “Poisoned Pipeline Execution”)
Identity & access	Least-privilege, short-lived, scoped credentials for every runner and service account	Overly broad or long-lived pipeline credentials are a top target once an attacker gets any foothold (OWASP CICD-SEC-2, CICD-SEC-6)
Artifact integrity	Sign commits and build artifacts; verify signatures before deploy	Without verification, a tampered artifact is indistinguishable from a legitimate one (OWASP CICD-SEC-9)
Build provenance	Generate signed provenance describing how an artifact was built (SLSA Build track)	Lets consumers verify an artifact came from the expected source and build process, not a tampered one
Secrets management	Centralized secrets manager, no plaintext secrets in config or environment files, automatic rotation	Leaked long-lived credentials are one of the most common real-world pipeline breach vectors
Logging & visibility	Centralized, tamper-resistant logs of every pipeline run and who triggered it	Without logs, a compromise can go undetected indefinitely (OWASP CICD-SEC-10)

Dependency and Supply-Chain Controls

Most real-world software supply-chain incidents start with a dependency, not a hand-written line of code. OWASP’s Top 10 CI/CD Security Risks names this “Dependency Chain Abuse” (CICD-SEC-3): attackers compromise or impersonate a package your build pulls in, and your pipeline happily builds it into your release.

Pin dependencies to exact versions, and where your package ecosystem supports it, to a content hash — not a floating version range.
Generate a Software Bill of Materials (SBOM) for every build so you can quickly answer “are we affected?” when a new CVE breaks.
Vet new and updated third-party components before they’re approved for use, per NIST SSDF practice PW.4 (“Reuse Well-Secured Software”), rather than letting any package anyone adds get built automatically.
Mirror or proxy public package registries internally so a registry-level compromise or a removed package can’t silently break or poison your builds.

Trade-off: strict pinning slows down picking up security patches automatically — you need a deliberate, scheduled update process (with automated vulnerability scanning) rather than relying on floating versions to “self-heal,” which is itself an attack surface.

Pipeline Execution and Configuration

OWASP calls out “Poisoned Pipeline Execution” (CICD-SEC-4) as a distinct risk: if an attacker can modify the pipeline definition itself — a CI config file, a build script, an included template — they can make the pipeline run arbitrary commands with whatever privileges that pipeline holds, often including access to production secrets.

Require code review on any change to pipeline configuration files, exactly as you would for application code.
Run workflows triggered by forked-repository pull requests with no access to secrets, or require explicit maintainer approval before they run at all.
Disallow pipeline steps from being defined by untrusted, dynamically fetched content at build time.
Address “Insufficient Flow Control” (CICD-SEC-1) by enforcing required approvals/gates between code merge and production deploy — a pipeline that can push straight from an unreviewed commit to production has no human check left in the loop.

Identity, Access, and Secrets

OWASP groups several related risks here: “Inadequate Identity and Access Management” (CICD-SEC-2), “Insufficient PBAC / Pipeline-Based Access Controls” (CICD-SEC-5), and “Insufficient Credential Hygiene” (CICD-SEC-6). The common thread is that pipelines, build agents, and service accounts routinely get more access than the specific job they run actually needs, and that access doesn’t expire on its own.

Give each runner and service account the minimum scope needed for its specific job — a job that only needs to read one repository should not hold a token that can push to all of them.
Prefer short-lived, automatically rotated credentials (OIDC-based cloud role assumption, for example) over static API keys and tokens.
Store secrets exclusively in a dedicated secrets manager, never in plaintext environment variables, config files, or source control.
Separate credentials and access between non-production and production pipelines; a compromised staging pipeline shouldn’t be able to reach production deploy keys.

This also maps directly onto NIST SSDF’s “Prepare the Organization” group, specifically PO.5, which calls for separating and hardening development, build, test, and distribution environments rather than running everything on shared, loosely controlled infrastructure.

Artifact Integrity and Build Provenance

“Improper Artifact Integrity Validation” (OWASP CICD-SEC-9) covers the gap where a build output is deployed without any check that it’s actually the artifact the pipeline produced, unmodified. The SLSA framework (slsa.dev) gives this a formal, incremental structure through its Build track levels:

SLSA Build L1: provenance (metadata describing how the artifact was built) exists and is distributed, but it isn’t signed — useful for catching honest mistakes, not for stopping a determined attacker.
SLSA Build L2: builds run on a dedicated, hosted build platform (not a developer’s laptop), and the provenance is cryptographically signed and tied to that platform — this is the level at which post-build tampering becomes detectable.
SLSA Build L3: the build platform additionally isolates each build run from every other run and keeps the signing material inaccessible to user-defined build steps — this is the level that defends against tampering during the build itself, including by insiders or compromised build definitions.

Practical controls that implement this:

Require signed commits so every change in history can be attributed to a verified identity (NIST SSDF PS.1).
Sign build artifacts and container images, and enforce signature verification as a hard gate before deployment, not an optional check.
Generate and publish cryptographic hashes or SLSA-format provenance for every release so downstream consumers (or your own deploy tooling) can verify it (NIST SSDF PS.2).
Archive release artifacts and their provenance/SBOM data so a future vulnerability disclosure can be traced back to exactly which builds are affected (NIST SSDF PS.3).

Trade-off: SLSA L3-equivalent build isolation is meaningfully harder to operate than L1 or L2 — it typically means adopting a managed, ephemeral build platform rather than long-lived self-managed runners. Most teams are better served by reaching L2 broadly (signed provenance from a hosted, non-developer-machine build) before chasing L3 everywhere.

Third-Party Services and Logging

Two more OWASP risks round out the list: “Ungoverned Usage of 3rd Party Services” (CICD-SEC-8) — pipeline integrations and marketplace actions adopted without security review, each one a new potential access path into your build — and “Insufficient Logging and Visibility” (CICD-SEC-10), where a compromise can persist undetected simply because no one is watching pipeline activity closely enough to notice it.

Maintain an approved allowlist of third-party CI actions/plugins, and pin them to a specific commit or version rather than a floating tag.
Review the permissions any new third-party integration requests before approving it, the same way you’d review an OAuth app request.
Centralize pipeline logs outside the CI system itself, so an attacker who compromises the CI platform can’t also erase the evidence.
Alert on anomalous pipeline behavior — new secrets accessed, unusual deploy targets, runs triggered outside normal hours — rather than only reviewing logs after an incident.

Vulnerability Response

NIST SSDF’s “Respond to Vulnerabilities” (RV) group is the piece teams most often skip because it isn’t about prevention — it’s about what happens after something is found. RV.1 calls for gathering vulnerability information from public sources and researchers and maintaining a disclosure policy; RV.2 calls for assessing and remediating each one based on actual risk; RV.3 calls for root-cause analysis so the same class of issue doesn’t recur. A secure pipeline that can’t quickly answer “which of our artifacts contain this dependency” — which is exactly what an SBOM and build provenance are for — turns a routine disclosure into a multi-week fire drill. If you’re also managing AI-assisted development, our AI-generated code security review checklist covers the related question of reviewing code your pipeline builds but nobody fully hand-wrote.

The Checklist

Dependencies pinned to exact versions or hashes, with an SBOM generated per build
New/updated third-party components vetted before approval
Pipeline configuration files reviewed like code, with no untrusted dynamic content executed at build time
Forked-PR workflows blocked from secrets or gated behind maintainer approval
Required approval gates between merge and production deploy
Least-privilege, short-lived credentials per runner/service account; no static long-lived tokens where avoidable
Secrets stored only in a dedicated secrets manager, never in plaintext config or source control
Production and non-production pipeline credentials fully separated
Commit signing required; signature verification enforced before merge where feasible
Build artifacts signed, with signature verification as a hard gate before deployment
Builds run on a dedicated, hosted build platform — not a developer workstation — targeting at least SLSA Build L2
Release artifacts and provenance/SBOM data archived for future vulnerability lookups
Third-party CI actions/plugins allowlisted and version-pinned
Pipeline logs centralized outside the CI platform, with alerting on anomalous activity
A documented vulnerability disclosure and response process, with root-cause analysis after remediation

Bottom Line

Securing a CI/CD pipeline against supply-chain attacks isn’t one control, it’s the accumulation of many small ones: pin what you depend on, treat pipeline config as code, scope credentials tightly, sign what you build, and log everything so a breach doesn’t stay invisible. OWASP’s Top 10 names the specific risks, SLSA gives you a concrete, incremental ladder for build integrity, and NIST’s SSDF ties it all into a broader secure-development lifecycle. None of these require buying a specific vendor’s product — they’re a checklist you can start implementing with the CI system you already have.

FAQ

What’s the single highest-priority control to start with?

Least-privilege, short-lived credentials for pipeline runners and service accounts. Overly broad, long-lived credentials are consistently the mechanism that turns a minor pipeline compromise into full production access.

What’s the difference between SLSA and an SBOM?

An SBOM (Software Bill of Materials) lists what components are in an artifact. SLSA provenance describes how the artifact was built — the build platform, process, and inputs — and, at higher levels, is cryptographically signed so it can’t be forged or altered after the fact. They’re complementary, not substitutes.

Do we need to reach SLSA Build L3 to be considered secure?

Not necessarily. L3 defends specifically against tampering during the build, including by insiders — a real but narrower threat than what L2 (signed provenance from a hosted build platform) already addresses. Most organizations get the majority of practical benefit by reaching L2 consistently before investing in L3’s build-isolation requirements.

Is dependency pinning enough to stop supply-chain attacks?

No. Pinning stops a dependency from silently changing out from under you, but it doesn’t catch a malicious package that was already malicious when you first pinned it. It needs to be paired with vetting new/updated components and scanning for known vulnerabilities on a deliberate schedule.

Where do these frameworks overlap?

Heavily. OWASP’s risks describe specific attack patterns in CI/CD systems; NIST’s SSDF describes organization-wide practices across the whole development lifecycle (of which pipeline security is one part); SLSA focuses narrowly and technically on verifying how a specific artifact was built. Using all three together covers the risk-identification, organizational-practice, and technical-verification angles respectively.

Sources

Artificial Intelligence, Engineering

What Are AI Coding Agents? Autonomous Developer Agents Explained in 2026

Posted on October 29, 2026 by Filip Keser

“AI coding agent” has become one of the most overused phrases in developer tooling, applied to everything from inline autocomplete to systems that write, test, and ship code with almost no human in the loop. Those are genuinely different things. This explainer draws only on vendors’ own documentation and announcements to describe what today’s most-discussed coding agents actually claim to do, where those claims are vendor-reported versus independently verified, and how agents differ structurally from the assistants most developers already use.

At a Glance

Category	Autonomy Level	Typical Use	Examples
Autocomplete	None — predicts the next tokens as you type	Inline suggestions inside an editor	Classic Copilot-style inline completion
Coding Assistant (chat)	Per-request — acts only when asked, one exchange at a time	Answer questions, explain code, generate a snippet to review	Copilot Chat, ChatGPT used interactively for code
Autonomous Coding Agent	Multi-step — plans a task, uses tools in a loop, works with limited supervision	Research a repo, write and test code across files, open a pull request	Devin, GitHub Copilot coding agent, Claude Code / Claude Agent SDK applications, OpenAI Codex cloud agent

What Makes an “Agent” Different From an Assistant

The field hasn’t settled on one precise definition, and vendors use the word loosely, but the pattern that recurs across official descriptions is a loop: the system gathers context on its own, decides what action to take, uses a tool (a shell, an editor, a browser, a test runner) to take that action, observes the result, and repeats until it decides the task is done — rather than producing one reply and stopping. Anthropic’s own framing for its Agent SDK captures this directly: “An agent is an application that completes a task by planning its own steps and calling tools that read files, run commands, or edit code.” A plain autocomplete model has no loop at all; it predicts tokens. A chat-based assistant has a loop of exactly one turn, bounded by what you ask in that message. An agent’s loop can run for minutes, and in some vendors’ products, run many steps unattended inside a sandboxed environment before a human looks at the result. That last part — acting inside a real environment with real tools, across multiple steps, with the human reviewing an outcome rather than approving each line — is the structural thing that separates an “agent” from an “assistant” in vendor usage, even though the industry still argues about where exactly the line falls.

Devin (Cognition)

Cognition introduced Devin in March 2024 as, in the company’s own words, “the world’s first fully autonomous AI software engineer.” Per Cognition’s announcement, Devin plans and executes complex engineering tasks inside a sandboxed environment that includes a shell, a code editor, and a browser; the company says it can learn an unfamiliar tool from documentation, recall context across steps, and fix its own mistakes, while reporting progress and taking feedback from a human along the way. Cognition’s demonstrated examples included building and deploying a small web app, fixing a bug in an open-source codebase, and resolving real GitHub issues.

On the benchmark claim: Cognition’s launch post reported that Devin resolved 13.86% of a 25%-random-subset of SWE-bench issues end-to-end and unassisted, against a previous best of 1.96% unassisted (4.80% for models given the exact files to edit). This is important to flag clearly: this is Cognition’s own self-reported number from its own 2024 announcement, measured on a subset it selected, and we did not find an independently reproduced, third-party verification of that specific figure. Treat it as a vendor claim, not an audited benchmark result, and note that SWE-bench methodology and leaderboard figures across the industry have been debated since. Cognition said a fuller technical report would follow; our research for this article did not surface that report.

GitHub Copilot Coding Agent

GitHub’s own documentation describes Copilot’s cloud-based agentic capability this way: “Copilot can research a repository, plan changes, and implement them in the background.” It edits files and runs tests and linters inside a cloud sandbox or an Actions-based runner, then opens a pull request once you’ve reviewed its proposed changes or once your prompt explicitly asks for one. A related feature, Agent Merge, watches an open pull request and can automatically address failing Actions workflows or merge conflicts. GitHub is explicit about the limits: an Actions-based session can only touch the single repository it was assigned to and is capped at 59 minutes; the agent works on one branch and opens one pull request per task; it requires write access to operate, and session logs are meant to support your review, not replace it. GitHub also states plainly that the agent “can produce incorrect or insecure code,” so output must be reviewed and tested before use. As of October 28, 2026, GitHub’s cloud agent experience requires a paid Copilot plan, subject to organization policy; our site’s own GitHub Copilot Review covers plan pricing and tiers in detail.

Claude Code and the Claude Agent SDK (Anthropic)

Anthropic’s documentation positions Claude Code as a terminal-based coding agent, and the Claude Agent SDK as a library that lets developers “build production AI agents with Claude Code as a library,” exposing the same agent loop, built-in tools (read, write, edit files, run commands, search the web), permissions system, and session management that power Claude Code itself, programmable in Python or TypeScript. Anthropic’s docs list specific supported capabilities rather than marketing superlatives: hooks that run custom code at points in the agent’s lifecycle, subagents that can be spawned for focused subtasks, Model Context Protocol (MCP) connections to external tools and data, and configurable permissions that control which tool calls run automatically versus which require approval. This is a notably narrower, more mechanism-focused description than some competitors’ launch posts — Anthropic’s framing centers on the loop-plus-tools architecture rather than end-to-end autonomy claims.

OpenAI Codex

OpenAI’s current documentation describes Codex as “OpenAI’s coding agent for software development,” included in ChatGPT Plus, Pro, Business, Edu, and Enterprise plans, capable of writing code from a description, explaining unfamiliar codebases, reviewing code for bugs and edge cases, debugging failures, and automating workflows like refactors, test generation, and migrations. OpenAI’s original introduction of the cloud agent (May 2025) described it in more autonomous terms: a “cloud-based software engineering agent that can work on many tasks in parallel,” where each task runs in an isolated sandbox preloaded with the user’s repository, with internet access disabled by default during execution (a June 2025 update allowed enabling it). The agent reads and edits files, runs test harnesses, linters, and type checkers, iterates on failing tests, and on completion commits its changes so a human can review them, request revisions, or open a GitHub pull request. OpenAI states plainly that users must manually review and validate all agent-generated code before integrating or running it, and that users cannot course-correct the agent mid-task — only after it finishes a run.

Comparing the Four

Agent	Vendor	Runs In	Opens PRs	Human Review Point
Devin	Cognition	Sandboxed environment (shell, editor, browser)	Yes, per Cognition’s description	During task, via real-time progress and feedback
Copilot coding agent	GitHub (Microsoft)	Cloud sandbox or GitHub Actions runner	Yes, after review or on request	Before and after PR creation; logs for review
Claude Code / Agent SDK	Anthropic	Local terminal, CI, or custom app via SDK	Depends on how the integrator builds it	Configurable via permissions system
Codex cloud agent	OpenAI	Isolated cloud sandbox preloaded with the repo	Yes, after task completion	After the agent commits; manual review required before use

Vendor Productivity Claims vs. Independent Evidence

Every vendor above has, at some point, made a productivity or capability claim in its own marketing — faster resolution of issues, higher benchmark scores, fewer human hours per task. Treat any specific percentage or benchmark score from a vendor’s own blog post or press release as a vendor claim unless it has been independently reproduced by a third party using a published, auditable methodology. Benchmark suites like SWE-bench have known methodology sensitivities (which subset is used, whether a model is given the exact files to change, whether runs are cherry-picked), and different vendors have reported results on different subsets and conditions, which makes cross-vendor comparison unreliable without reading each methodology section carefully. We have not independently verified any vendor’s specific productivity multiplier in this piece, and none is repeated here as established fact.

The Real Risks

Letting a tool plan, edit, and commit code across a real codebase with limited supervision raises security and review questions that go well beyond what this explainer can cover responsibly. Every vendor above explicitly tells users to review and test agent output before relying on it, which is itself a signal about current reliability. For a detailed look at how often AI-generated code introduces vulnerabilities, how those risks map to the OWASP Top 10 for LLM Applications, and a practical review checklist for teams adopting these tools, see our companion piece, AI-Generated Code Security in 2026.

The Bottom Line

An autonomous coding agent isn’t a smarter autocomplete — it’s a system built around a plan-act-observe loop that uses real tools inside a real (usually sandboxed) environment, with the human reviewing outcomes rather than approving every keystroke. Devin, GitHub’s Copilot coding agent, Claude Code and the Claude Agent SDK, and OpenAI’s Codex cloud agent all fit that structural pattern, according to their own vendors’ documentation, even though they differ in where they run, how much a human can intervene mid-task, and how much marketing language surrounds their launch. The specific productivity numbers are the part to be skeptical of; the architectural shift toward tool-using, multi-step agents is the part that’s well documented and, at this point, not really in dispute.

FAQ

Is GitHub Copilot’s autocomplete the same thing as its coding agent?
No. Copilot’s inline autocomplete predicts code as you type with no planning loop; Copilot’s coding agent is a separate, cloud-based feature that researches a repo, plans changes, implements them, and can open a pull request.

Can these agents be trusted to merge code without a human?
Every vendor covered here explicitly states that output should be reviewed and tested before use, and most products require an explicit human action (merging a PR, approving a session) before code reaches production.

Is the Devin SWE-bench number (13.86%) still accurate today?
That figure comes from Cognition’s original March 2024 announcement on a specific 25% subset of SWE-bench, measured under specific conditions. We did not find an independently audited, current reproduction of it, so it should be read as a historical vendor claim, not a current, verified benchmark.

What’s the difference between Claude Code and the Claude Agent SDK?
Claude Code is the terminal application for interactive, daily use. The Claude Agent SDK is a library that lets developers embed the same underlying agent loop and tools inside their own Python or TypeScript applications.

Do these agents need internet access to work?
It varies. OpenAI’s Codex cloud agent disables internet access by default during a task (with an option to enable it), largely for security reasons; other agents’ network access depends on how the sandbox or environment is configured by the vendor or integrator.

Sources

Engineering

GitHub Actions vs GitLab CI/CD vs CircleCI: Choosing a CI/CD Pipeline in 2026

Posted on October 29, 2026October 29, 2026 by Filip Keser

Every team that ships code eventually asks the same question: which CI/CD platform is worth paying for? In 2026 the honest answer is “it depends on where your code already lives and how you want to pay for compute,” because GitHub Actions, GitLab CI/CD, and CircleCI have converged on very similar feature sets but very different pricing models. This comparison sticks to what each vendor states on its own pricing and documentation pages as of October 2026, so you can see the real free-tier limits and overage costs before you commit.

At a Glance

Platform	Starting Price	Free Tier	Best For
GitHub Actions	Free for public repos; $0.002–$0.062 per minute beyond included minutes on private repos	~2,000 min/month (GitHub Free), ~3,000 min/month (Pro/Team)	Teams already hosting their code on GitHub
GitLab CI/CD	Free: $0/user/month — Premium: $29/user/month (annual)	400 compute minutes/month (Free), 10,000/month (Premium)	Teams wanting source control, CI/CD, and security scanning in one platform
CircleCI	Free: $0/month — Performance: from $15/month	30,000 credits/month on both Free and Performance	Teams wanting a CI-focused tool with fine-grained machine types, usable with any Git host

How CI/CD Pricing Actually Works

The three platforms meter usage in three different units, which makes head-to-head comparison harder than it should be. GitHub Actions bills in minutes, with a per-minute rate that depends on the operating system and machine size of the runner. GitLab bills in compute minutes, calculated as job duration divided by 60 and multiplied by a runner-specific “cost factor” — GitLab is explicit that this cost factor is not a monetary price, just a usage multiplier. CircleCI bills in credits, an abstract unit that is consumed at different rates depending on the resource class (CPU, memory, and OS) a job uses. None of these units convert cleanly into the others, so the only reliable way to compare cost is to estimate your own typical job mix against each vendor’s calculator.

GitHub Actions in 2026

GitHub Actions remains free for workflows on public repositories, with no minute limit. For private repositories, GitHub Free includes roughly $12 of monthly compute (about 2,000 minutes on a standard 2-core Linux runner at $0.006/minute), GitHub Pro and Team include roughly $18 (about 3,000 minutes), and GitHub Enterprise Cloud includes $300 of compute (roughly 50,000 minutes). Per-minute rates on standard hosted runners range from $0.002 for a 1-core Linux “slim” runner up to $0.062 for a macOS runner; GitHub also offers much larger hosted runners (up to 96-core Linux and Windows, plus GPU runners) at correspondingly higher per-minute rates. As of January 1, 2026, GitHub cut hosted-runner prices by up to 39% depending on machine type. A separate plan to start charging $0.002/minute for self-hosted runner usage on private repositories, originally announced for March 1, 2026, has been postponed while GitHub re-evaluates the approach.

Pros:

No separate CI tool to connect — workflows live in the same repo and UI as your code and pull requests
Free, unlimited minutes for public/open-source repositories
Enormous marketplace of reusable community Actions

Cons:

Included minutes on the Free and Pro tiers are modest for active private-repo teams
macOS runners are the most expensive per minute of any platform compared here
Tied to GitHub as your source-control host

A minimal workflow file looks like this:

name: CI
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: echo "Hello CI"

GitLab CI/CD in 2026

GitLab’s CI/CD is built into the same product as its Git hosting, issue tracking, and security scanning, which is the platform’s core pitch: one tool instead of several. On GitLab.com, the Free tier includes 400 compute minutes per month at no cost. Premium, at $29 per user per month billed annually, includes 10,000 compute minutes per month. Ultimate is custom-priced (GitLab does not publish a per-user rate) and includes 50,000 compute minutes per month. Runner cost factors on GitLab.com’s hosted runners range from 1x for a small Linux x86-64 runner up to 12x for a 2xlarge Linux runner or a beta macOS M2 Pro large runner; GPU runners carry a 7x factor. Public open-source projects get a 0.5x discount, and forks of those projects get a steep 0.008x discount, making GitLab notably cheap for qualifying open-source work.

Pros:

CI/CD, source control, and security scanning (SAST, dependency scanning) in a single integrated platform
Transparent, well-documented cost-factor formula
Strong discounts for public open-source projects

Cons:

Free tier’s 400 minutes/month is the smallest of the three platforms
Ultimate tier pricing isn’t public, which complicates budgeting at the high end
Per-seat pricing on Premium can get expensive for larger engineering orgs regardless of CI usage

CircleCI in 2026

CircleCI is a dedicated CI/CD product that connects to GitHub, GitLab, or Bitbucket rather than requiring a specific host. Its Free plan costs $0/month and includes 30,000 credits/month, 5 active users, 30x concurrency, 1GB network and 2GB storage, 1,000 Smarter Testing “atoms” (CircleCI’s test-splitting/insights unit), and 5 self-hosted runner tasks. The Performance plan starts at $15/month, also includes 30,000 credits/month but raises concurrency to 80x and adds larger resource classes; additional credits cost $15 per 25,000 (roughly $0.0006 per credit), and CircleCI also sells prepaid bulk credits (1 million for $600 or 5 million for $3,000, expiring after 12 months). The Scale plan is custom-priced, billed annually, and removes the concurrency cap entirely while adding GPU resource classes (Nvidia T4, A10G, V100, and P4) and SSO/config policies.

Pros:

Works with any major Git host, not locked to one vendor’s repos
Fine-grained resource classes (Docker, VM, Arm, macOS, GPU) and reusable config via “orbs”
Generous free-tier concurrency (30x) compared to the other two platforms’ defaults

Cons:

Credits are an abstract unit that takes effort to translate into a dollar budget
No native source control, issue tracking, or security scanning — it’s CI/CD only
Scale-tier pricing is custom and not published

Pricing Comparison

Plan	Monthly Price	Included Usage	Overage Cost
GitHub Free	$0	~2,000 min (2-core Linux equivalent)	$0.002–$0.062/min by runner type
GitHub Pro/Team	$4–$21/user	~3,000 min	$0.002–$0.062/min by runner type
GitHub Enterprise Cloud	Custom	~50,000 min ($300 compute)	$0.002–$0.062/min by runner type
GitLab Free	$0	400 compute min	Purchase additional minute packs
GitLab Premium	$29/user (annual)	10,000 compute min	Purchase additional minute packs
GitLab Ultimate	Custom	50,000 compute min	Purchase additional minute packs
CircleCI Free	$0	30,000 credits	Upgrade plan required
CircleCI Performance	From $15	30,000 credits	~$0.0006/credit ($15 per 25,000)
CircleCI Scale	Custom (annual)	Custom	Custom

Who Should Pick Which

Pick GitHub Actions if your code already lives on GitHub and you want CI/CD with zero extra integration work, especially for open-source projects where minutes are free.
Pick GitLab CI/CD if you want source control, CI/CD, and security scanning under one roof and can budget per-seat pricing, or if you qualify for GitLab’s open-source discounts.
Pick CircleCI if you want a best-of-breed CI/CD tool that works with whatever Git host you use, need high concurrency on a budget, or want granular control over machine resource classes including GPUs.

The Bottom Line

There is no universal winner here — the right platform is mostly determined by where your repositories already live and how predictable your build volume is. GitHub Actions wins on convenience for GitHub-hosted projects and is unbeatable for public repos. GitLab wins if you want an all-in-one DevSecOps platform and can absorb per-seat licensing. CircleCI wins on flexibility and concurrency if you want a dedicated CI/CD specialist rather than a bundled platform. Whichever you choose, run your actual job durations through the vendor’s own pricing calculator before committing — the free tiers are generous enough for side projects but can be exhausted quickly by an active team.

FAQ

Is GitHub Actions really free?
It’s free with unlimited minutes for public repositories. Private repositories get a limited number of free minutes per month depending on your GitHub plan, then pay per minute beyond that.

What is a GitLab “compute minute” versus a regular minute?
A compute minute is job duration divided by 60, multiplied by a runner-specific cost factor. A job on a more powerful runner (like a 2xlarge machine) consumes compute minutes faster than the same job on a small runner.

How much is a CircleCI credit worth in dollars?
CircleCI doesn’t publish a flat per-credit price for the Free plan, but on the Performance plan, additional credits cost $15 per 25,000, which works out to about $0.0006 per credit.

Can I use CircleCI with GitHub or GitLab repos?
Yes. Unlike GitHub Actions and GitLab CI/CD, which are built into their respective hosting platforms, CircleCI is host-agnostic and connects to GitHub, GitLab, or Bitbucket.

Did GitHub Actions pricing change in 2026?
Yes. Hosted-runner per-minute rates dropped by up to 39% on January 1, 2026, depending on machine type. A planned charge for self-hosted runner usage on private repos, originally set for March 1, 2026, was postponed.

Sources

marketing, software, technology

Mailchimp Review: Features, Pricing, and Whether the Free Plan Still Makes Sense in 2026

Posted on October 22, 2026October 27, 2026 by Filip Keser

Mailchimp built its reputation as the default free email marketing tool for small businesses, but under Intuit’s ownership (since 2021) that free plan has been quietly and repeatedly cut down. In 2026 it sits at 250 contacts and 500 sends a month — a fraction of what it offered a few years ago. Here’s what’s actually in each tier now, and whether the platform’s broader feature set still justifies its price once you outgrow free.

Get Mailchimp: Visit the official Mailchimp pricing page →

At a Glance

Plan	Starting Price	Contacts (entry tier)	Best For
Free	$0/mo	250	Testing the platform with a very small list
Essentials	$13/mo (first 12 months)	500	Solo creators needing scheduling and basic automation
Standard	$20/mo (first 12 months)	500	Small businesses wanting branded templates and segmentation
Premium	$350/mo (first 12 months)	10,000+	Larger teams needing SSO, phone support, and advanced testing

Short Verdict

Mailchimp remains a capable, well-rounded email platform — 4.5/5 across 17,580 Capterra reviews backs that up — but its free plan is no longer a meaningful starting point for a growing list. Automations were removed from the free tier entirely in June 2025, and the contact cap has fallen from 2,000 a few years ago to 250 today, with unsubscribed contacts now counting against that limit. Budget for Essentials or Standard from day one rather than planning around Free.

What It Is

Mailchimp is an email and SMS marketing platform covering campaign building, automation flows, audience segmentation, landing pages, and reporting, with a drag-and-drop editor and a library of pre-built templates. It’s owned by Intuit, which has been steadily integrating it with QuickBooks and Intuit’s small-business suite rather than running it as a fully standalone product.

Pricing Breakdown

Mailchimp’s pricing scales with contact count, so the dollar figures below are entry-level rates for the smallest list size at each tier; larger lists cost proportionally more and 10,000+ contact lists get a 15% discount on the first 12 months:

Free — $0/month: up to 250 contacts, 500 sends/month (capped at 250/day), 1 user seat, 1 audience, limited pre-built templates, popup forms, and email support for the first 30 days only. No scheduling, no automations, no branding removal.
Essentials — from $13/month (14-day free trial): 3 seats, 3 audiences, 24/7 email and chat support, email scheduling, branding removal, marketing automation up to 4 flow steps, and an optional SMS add-on.
Standard — from $20/month (14-day free trial): 5 seats, 5 audiences, 4 user roles, one onboarding session, up to 200 automation flows, generative AI tools at no extra cost, custom-coded templates, branded templates, custom reports, dynamic content, and predictive segmentation.
Premium — from $350/month: unlimited seats and audiences, 5 roles, enterprise SSO, phone and priority support, 4 onboarding sessions plus a dedicated specialist, A/B and multivariate testing, and premium migration services.

Flag for readers: Mailchimp’s own pricing page doesn’t publish the renewal rate after the first 12 months on paid plans — only the discounted introductory price. Check your renewal price in-account before committing, since it will be higher than the number advertised on the pricing page.

Pros

Broad feature set at Standard and above: predictive segmentation, dynamic content, and generative AI tools included at no extra cost.
Large third-party integration ecosystem and strong brand recognition, which matters for agencies pitching clients a known name.
Strong review volume and rating (4.5/5 from 17,580+ Capterra reviews) reflecting a mature, stable product.
14-day free trial on paid tiers to test automation and template features before paying.

Cons

The free plan has been cut repeatedly — down to 250 contacts and 500 sends/month, with automations removed entirely in June 2025.
Unsubscribed contacts now count toward your contact limit, inflating your effective list size and pushing you into a higher tier faster.
Pricing scales quickly with list size, and the advertised rate is a 12-month introductory price, not the ongoing cost.
Premium’s $350/month entry price is a steep jump from Standard for teams that need just one or two enterprise features like SSO.

Who It’s a Good Fit For

Small businesses and solo marketers who are past the testing stage and ready to pay for Essentials or Standard get a genuinely full-featured platform, especially if they want AI-assisted content and segmentation without upgrading to an enterprise-priced tool.

Who Should Look Elsewhere

Anyone planning to run their list entirely on a free plan should look at competitors with more generous free tiers, since Mailchimp’s 250-contact, no-automation free plan is no longer competitive for that use case.

Bottom Line

Mailchimp’s paid tiers still deliver a mature, well-reviewed email marketing platform with real AI and segmentation depth. Its free plan, though, has shrunk to the point of being a demo rather than a usable starting tier — go in expecting to pay from the start, and double-check your renewal price before your list grows past the introductory window.

How Mailchimp Compares to Klaviyo

Mailchimp and Klaviyo both cap their free tier at 250 contacts, but what’s included at that cap is very different. Mailchimp’s free plan limits you to 500 sends a month and removed automations entirely in June 2025, while Klaviyo’s free plan keeps full automation and segmentation tools active at 250 email contacts and throws in 150 SMS/MMS credits on top. The gap persists once you pay: Mailchimp’s Essentials starts at $13/month for 500 contacts but still caps automation at four flow steps, while Klaviyo switches to usage-based pricing starting at $20/month for the 251–500 contact bracket with the same unrestricted automation you had for free. Klaviyo’s bigger advantage for e-commerce is that its automations trigger off real purchase and browsing behavior synced from Shopify or WooCommerce, not just email engagement — something Mailchimp’s general-purpose automation doesn’t natively build around. Mailchimp’s case is breadth and predictability: flat tier pricing, a larger non-commerce feature set (landing pages, a broader template library), and a bigger brand name agencies can pitch to non-technical clients.

Spec	Mailchimp	Klaviyo
Free plan	250 contacts, 500 sends/mo, no automation	250 email contacts + 150 SMS/MMS credits, full automation & segmentation
Entry paid price	$13/month (Essentials, 500 contacts, automation capped at 4 steps)	$20/month (251–500 contact bracket, full feature set)
Pricing model	Flat tiers (Essentials/Standard/Premium) scaling by contact count	Continuous scaling by contact count and message volume
Best for	General broadcast email + SMS marketing across any business type	E-commerce stores wanting automation built on purchase/browsing data

Frequently Asked Questions

Is Mailchimp free forever? Yes, the Free plan has no time limit, but it’s capped at 250 contacts and 500 sends a month with no automation features.
Does Mailchimp’s free plan include automation? No — Mailchimp removed automations from the free tier entirely in June 2025; you need Essentials ($13/month+) or higher for automation flows.
Is Mailchimp worth it if I only need basic email marketing for a very small list? Possibly not if you want automation at that size — Klaviyo’s free plan matches Mailchimp’s 250-contact cap but keeps full automation active, which may suit a small e-commerce list better.
How does Mailchimp’s pricing scale as your list grows? It scales in flat tiers (Essentials, Standard, Premium) based on contact count, and the advertised price is a discounted first-12-months rate — Mailchimp doesn’t publish the renewal rate upfront, so check it in-account before committing.

Sources

marketing, software, technology

Kit (Formerly ConvertKit) Review: Features, Pricing, and Whether the Free Plan Is Enough for Creators in 2026

Posted on October 21, 2026October 27, 2026 by Filip Keser

ConvertKit rebranded to Kit a couple of years ago, but the pitch hasn’t changed: email marketing built specifically for creators selling newsletters, courses, and digital products rather than for e-commerce stores. Here’s what the free plan actually covers, what Creator and Creator Pro cost as your list grows, and who the platform fits best.

Get Kit: Visit the official Kit pricing page →

At a Glance

Plan	Price (1,000 subscribers)	Users	Best For
Free (Newsletter)	$0, up to 10,000 subscribers	1	Creators starting a newsletter who need unlimited sends without a bill
Creator	$33/month billed annually ($39/month billed monthly)	2	Creators who need automations, sequences, and integrations
Creator Pro	$66/month billed annually ($79/month billed monthly)	Unlimited	Teams and creators who want engagement scoring and advanced analytics

Rating: 4.6/5 from 244 reviews on Capterra. Free plan subscriber cap: 10,000 — unusually generous for a free email tier. Prices above are for 1,000 subscribers and rise in brackets as your list grows; Kit’s own pricing page does not list a flat rate for every subscriber count.

Short Verdict

Kit’s free plan is the most generous entry point of any major email platform reviewed on this site — 10,000 subscribers, unlimited sends, and the ability to sell digital products, all without a credit card. The catch is that automations and integrations — the features that turn a newsletter into an actual marketing system — are locked to the paid Creator plan. For a creator who’s outgrown “just send the newsletter” and wants sequences, tagging-based automation, and an app store full of integrations, Creator at $33 to $39/month (depending on billing cadence) for 1,000 subscribers is competitive. Creator Pro’s jump to $66 to $79/month is harder to justify unless you specifically need engagement scoring or unlimited team seats.

What It Is

Kit (the company formerly known as ConvertKit) is an email marketing platform built for individual creators — newsletter writers, course creators, podcasters — rather than for retail e-commerce stores like Klaviyo or Omnisend target. It bundles email broadcasts, a visual automation builder, landing pages and forms, and commerce tools for selling digital products and paid subscriptions directly from your audience, without needing a separate website or checkout tool.

Pricing Breakdown

The Free plan (branded “Newsletter”) covers up to 10,000 subscribers with unlimited email sends, one automation, and one email sequence — but no integrations with other apps. Creator starts at $33/month on annual billing (or $39/month billed monthly) for 1,000 subscribers and removes the automation and sequence limits entirely, adding unlimited Visual Automations, the Kit App Store’s integrations, subscriber polls, SMS marketing, and removal of Kit branding from emails and forms. Creator Pro starts at $66/month annually (or $79/month monthly) at the same 1,000-subscriber tier and adds Subscriber Signals engagement scoring, deliverability reporting, unlimited team seats, and a dedicated Growth Concierge. Both paid plans’ prices scale up in brackets as your subscriber count grows — third-party pricing trackers that mirror Kit’s own calculator put Creator at roughly $199/month and Creator Pro at roughly $279/month by 25,000 subscribers, though you should confirm the exact figure for your list size directly at kit.com/pricing rather than rely on a snapshot. Selling digital products or subscriptions carries an additional 3.5% + 30¢ transaction fee plus Kit’s own 0.6% fee on every plan, and the Paid Recommendations feature takes a 23.5% cut on Creator and Pro (it isn’t available on Free).

Features That Matter

Visual Automations: a drag-and-drop builder for sequences that trigger on subscriber behavior — tags, purchases, link clicks.
Commerce: sell digital products with automatic delivery, run paid newsletter subscriptions, or take on newsletter sponsorships, all inside the same platform.
Recommendations: get placed in other creators’ sign-up flows, and earn through Paid Recommendations when referred subscribers sign up for creators you promote.
Landing pages and forms: build sign-up pages without needing a separate website.

Pros

Free plan’s 10,000-subscriber cap is the most generous among major email platforms
Built-in commerce tools mean creators can monetize without bolting on Gumroad or a separate checkout
4.6/5 on Capterra reflects strong satisfaction specifically from solo creators and small teams
Recommendations network helps smaller newsletters grow through other creators’ audiences

Cons

Automations and third-party integrations are locked out of the free plan entirely
Transaction fees stack on top of the subscription for anyone selling digital products
Creator Pro’s price jump is steep relative to what it adds over Creator
Built for creators first — stores needing deep e-commerce behavioral data should look at Klaviyo instead

Who It’s a Good Fit For

Newsletter writers, course creators, and podcasters who want to grow an audience and sell directly to it — without e-commerce-specific needs — get the most out of Kit’s free-to-Creator path.

Who Should Look Elsewhere

Retail and e-commerce stores that need automation tied to purchase and browsing behavior are better served by Klaviyo or Omnisend. Anyone who needs automations on a true $0 budget will also hit Kit’s free-plan wall immediately, since sequences beyond one require upgrading.

Bottom Line

Kit’s free plan is genuinely useful up to 10,000 subscribers, and Creator is reasonably priced once automations become necessary. Just budget for the transaction fees if you plan to sell through it, and check the current-tier price for your actual list size before assuming the 1,000-subscriber rate holds.

How Kit Compares to Mailchimp

Kit and Mailchimp both offer a free tier, but the gap between them is stark: Kit’s free plan covers up to 10,000 subscribers with unlimited sends, while Mailchimp’s free plan has shrunk to 250 contacts and 500 sends a month with automations removed entirely in June 2025. Where Mailchimp pulls ahead is breadth — Essentials unlocks basic automation at $13/month (first 12 months) for 500 contacts, versus Kit’s Creator plan at $33/month (annual) for 1,000 subscribers before any automation becomes available. Creators who mostly send a newsletter and occasionally sell a product fit Kit’s model; businesses wanting a broader marketing suite with CRM ties (Mailchimp is owned by Intuit) may prefer Mailchimp’s path.

Spec	Kit	Mailchimp
Free plan limit	10,000 subscribers, unlimited sends	250 contacts, 500 sends/month
Automations on free plan	Just 1 automation, 1 sequence	None (removed June 2025)
Entry paid price	$33/month annual, 1,000 subscribers — unlocks unlimited automations	From $13/month (first 12 months), 500 contacts — up to 4-step automations
Best for	Newsletter and course creators selling direct to their audience	Small businesses wanting a broader marketing suite

Frequently Asked Questions

Is Kit’s free plan actually free forever? Yes, up to 10,000 subscribers with unlimited sends and no credit card required — but it’s limited to one automation and one sequence, with no third-party integrations.
Does Kit or Mailchimp have better free-plan automation? Neither offers real automation for free: Mailchimp removed it entirely in June 2025, and Kit caps free accounts at one automation. Both require a paid plan — Mailchimp’s Essentials at $13/month or Kit’s Creator at $33/month — for anything more.
Is Kit worth it if I only send a newsletter and don’t sell anything? Yes — the free plan’s 10,000-subscriber cap and unlimited sends cover that use case without ever needing to pay.
Which has the larger free-tier contact limit, Kit or Mailchimp? Kit, by a wide margin — 10,000 subscribers versus Mailchimp’s 250 contacts.

Sources

platform, technology

Integrations, Connectors, and MCP: What They Are and Why MCP Matters

Posted on October 16, 2026 by Filip Keser

At a Glance

Term	What It Means
Integration / connector	A built or pre-built link between two apps so data or actions flow between them automatically
iPaaS	A platform (Zapier, Workato, Make) that ships many pre-built connectors instead of custom point-to-point code
MCP (Model Context Protocol)	An open standard, released by Anthropic on November 25, 2024, for connecting AI applications to external tools and data sources
MCP governance	Donated by Anthropic to the Agentic AI Foundation, a Linux Foundation directed fund, on December 9, 2025

Short Verdict

“Integrations” and “connectors” are the decades-old problem of getting software to talk to other software; MCP is a 2024 attempt to solve a newer, narrower version of that same problem specifically for AI assistants and agents. The two are related but not interchangeable — an iPaaS tool like Zapier connects apps to each other, while MCP standardizes how an AI model connects to tools and data in the first place. Both matter for the same underlying reason: bespoke, one-off integrations don’t scale, and a shared standard means something gets built once and reused everywhere.

Integrations and Connectors, in Plain Terms

An integration is simply a link that lets two pieces of software exchange data or trigger actions in each other — a new row in your CRM creating a task in your project-management tool, or a form submission landing in a spreadsheet. Historically, building these required custom, point-to-point code for every single app pair, which doesn’t scale once you have more than a handful of tools. That’s the gap an iPaaS (integration-platform-as-a-service) fills: as Zapier’s own explainer defines it, iPaaS is “a cloud-based software service solution that lets organizations connect different applications” across cloud, on-premises, and hybrid environments, typically through pre-built connectors and low/no-code tools rather than hand-coded integrations. Zapier itself is a familiar example — its current pricing runs from a Free plan (100 tasks/month, unlimited workflows) up through Professional at $19.99/month billed annually, Team at $69/month billed annually, and a custom Enterprise tier, per Zapier’s own pricing page. Workato, by contrast, markets itself as an “Enterprise iPaaS” aimed at larger organizations automating workflows like Salesforce-to-NetSuite quote-to-cash processes, serving both IT and what it calls “citizen integrator” business users, per its own documentation.

What MCP Actually Is

The Model Context Protocol was announced and open-sourced by Anthropic on November 25, 2024, created by Anthropic engineers David Soria Parra and Justin Spahr-Summers. Per Anthropic’s own announcement, even the most capable AI models are “cut off from data trapped in information silos and legacy systems,” and historically every new data source needed its own bespoke integration — which doesn’t scale any better for AI assistants than it ever did for regular software. MCP’s pitch is to replace that fragmentation with what Anthropic calls “one protocol” and a “universal, open standard” for connecting AI systems to data sources, tools, and workflows.

The current official definition, from the independently-governed modelcontextprotocol.io documentation site, puts it simply: MCP “is an open-source standard for connecting AI applications to external systems.” The docs use a specific, official analogy — not just press shorthand — stating directly: “Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.”

How It’s Built: Hosts, Clients, and Servers

Per MCP’s official architecture specification, the protocol follows a client-host-server model built on JSON-RPC. The host is the application a person actually uses (Claude Desktop, for example), which creates and manages clients and enforces security policy and user consent. The client is created by the host, with a strict one-to-one relationship to a single server, handling protocol negotiation and keeping servers isolated from each other. The server exposes “resources, tools and prompts” — the protocol’s three core primitives — and can run locally or remotely. A key design principle stated directly in the spec: “Servers should not be able to read the whole conversation, nor ‘see into’ other servers” — the host keeps full conversation context, and servers only see what’s relevant to their own job. In practice, tools are actions the model can call, resources are contextual data the application can read, and prompts are reusable templated instructions — all negotiated when a session starts.

Why It Matters — and Who’s Adopted It

The official docs frame the benefit differently depending on audience: for developers, MCP “reduces development time and complexity” when building or integrating with an AI application; for AI applications themselves, it provides access to “an ecosystem of data sources, tools and apps”; for end users, it means more capable assistants that can access their data and take action on their behalf. The tagline the docs use for the ecosystem effect is “build once and integrate everywhere” — instead of every AI vendor building a custom Slack integration, a Google Drive integration, and a GitHub integration from scratch, one MCP server for each can work across any MCP-compatible AI application.

Adoption moved quickly after the November 2024 launch: Cursor added MCP server support in February 2025, Microsoft brought MCP into Copilot Studio and Cloudflare enabled customers to deploy remote MCP servers by around April 2025, and OpenAI added MCP support to its Agents SDK that same month, with modelcontextprotocol.io’s current documentation now listing ChatGPT among MCP-supporting AI assistants and linking to OpenAI’s own MCP developer docs. Visual Studio Code and Cursor are both listed as MCP-supporting development tools on the current official docs. One figure we’re deliberately not citing precisely: the number of community-built MCP servers now in existence. Estimates circulating across different third-party sources range anywhere from roughly 1,400 to over 11,000, depending on methodology and date — none of that is an Anthropic- or modelcontextprotocol.io-published figure, so treat any specific server count you see elsewhere as a rough, fast-moving estimate rather than a settled number.

The most significant recent change is governance, not adoption: on December 9, 2025, Anthropic donated MCP to the Agentic AI Foundation (AAIF), described as a directed fund under the Linux Foundation, with Anthropic, Block, and OpenAI as founding co-sponsors and Google, Microsoft, AWS, Cloudflare, and Bloomberg named as supporters. MCP’s existing maintainer structure and technical direction are stated to remain unchanged under this arrangement — the Linux Foundation’s role is custodial, not directive. This is independently corroborated by outside reporting beyond Anthropic’s own channels, which is part of why it’s credible as more than a one-source claim.

Bottom Line

Integrations and iPaaS tools solve how regular software talks to other software; MCP solves the newer, specific problem of how an AI assistant talks to your tools and data without a bespoke integration for every single one. Less than two years after its November 2024 launch, MCP has been adopted across a wide swath of the AI tooling landscape and transitioned to vendor-neutral, Linux Foundation-adjacent governance — a sign it’s being treated as infrastructure rather than a single vendor’s side project. If you’re building or buying AI tooling today, whether a connector supports MCP is a reasonable question to ask, in the same way “does this integrate with Zapier” became a reasonable question for SaaS tools a decade ago.

Sources

Anthropic — Introducing the Model Context Protocol (original Nov 25, 2024 announcement)
Model Context Protocol — official introduction (USB-C analogy, ecosystem benefits)
Model Context Protocol — official architecture specification
Zapier — What Is iPaaS?
Zapier — Pricing
Workato — What Is Workato
The Batch (DeepLearning.AI) — OpenAI adopts MCP
Microsoft — MCP in Copilot Studio

Google, management, marketing, platform, software, software online review, technology

deepmind.google

Posted on December 2, 2025October 27, 2026 by Filip Keser

https://deepmind.google/models/gemini/pro/

https://deepmind.google/models

https://deepmind.google

https://aistudio.google.com/

https://gemini.google.com/

https://jules.google.com/

software online review

Roadmap and business roadmap

Posted on November 1, 2025October 27, 2026 by Filip Keser

A roadmap is a strategic plan that defines a goal or desired outcome and includes the major steps or milestones needed to reach it. It also serves as a communication tool, a high-level document that helps articulate strategic thinking—the why—behind both the goal and the plan for getting there.

A business roadmap is a visual representation of your business strategy. It outlines the steps, goals, initiatives, and milestones needed to achieve your long-term plans. A business roadmap is also more tactical, focusing on how you will reach your objectives.

Eight Bukets – Challenge

Six – Create Value – Capture Value

I’m giving you the map, now you must walk the path

https://youtu.be/qllWAheHkms?si=fCkbOSRuRO5kZ0ol

The 80 – 20 Rule

https://www.investopedia.com/terms/1/80-20-rule.asp

software online review

Nvidia Dgx Spark

Posted on October 25, 2025October 27, 2026 by Filip Keser

https://marketplace.nvidia.com/en-us/enterprise/personal-ai-supercomputers/

https://marketplace.nvidia.com/en-us/developer/dgx-spark/

https://www.nvidia.com/en-us/

software online review

Project Sor

Posted on November 25, 2024October 27, 2026 by Filip Keser

software, software online review, technology

software-online-review

Posted on August 18, 2022October 27, 2026 by Filip Keser

software-online-review

software online review

software-online-review

Posted on July 22, 2022October 27, 2026 by Filip Keser

NEED CONSTRUCTION WORKER – https://software-online-review.com/startup-online-hiring-scheme-online/ARCHITECTS & SOFTWARE DEVELOPERS

https://www.delawareinc.com/

https://www.sba.gov/

https://eqvista.com/

https://www.ycombinator.com/library/Ek-stages-of-startups

https://www.investopedia.com/articles/personal-finance/011216/llc-vs-incorporation-inc-which-should-i-choose.asp

https://www.investopedia.com/terms/i/incorporate.asp

https://www.investopedia.com/terms/a/acquisition.asp

https://system1.com/

https://www.semrush.com/

https://www.theverge.com/2019/12/4/20994361/google-alphabet-larry-page-sergey-brin-sundar-pichai-co-founders-ceo-timeline

https://www.britannica.com/topic/Google-Inc

https://www.cnbc.com/2018/09/04/8-surprising-facts-you-might-not-know-about-googles-early-days.html

https://en.wikipedia.org/wiki/PageRank

https://www.google.com/search/howsearchworks/

https://www.google.com/search/howsearchworks/our-approach/ads-on-search/

https://developers.google.com/search

https://pagespeed.web.dev/

https://www.catchpoint.com/

https://search.google.com/search-console/about

WordPress VIP

Site Kit by Google – Analytics, Search Console, AdSense, Speed

https://sitekit.withgoogle.com/

https://www.investopedia.com/articles/markets/011516/top-5-google-shareholders-goog.asp

https://www.nasdaq.com/market-activity/stocks/goog

https://startup.google.com/programs/accelerator/

https://developers.google.com/community/accelerators

https://www.jetbrains.com/idea/

https://www.jetbrains.com

https://neuechair.com/

Neue™

https://www.google.com/

The 5 stages of a startup

Solving the problem. Running a successful business is all about producing something that solves a problem. …
2. Development. This is where it starts getting serious. …
Entering the market. …
Scaling. …
Maturity.

Buy it Now

https://sedo.com/us/

https://sedo.com/search/?keyword=software-online-review.com

https://www.trustpilot.com/review/software-online-review.com

Uncut Diamond

This article breaks down everything you need to know about uncut diamonds and how you can make a smart investment.

Filling in some gaps in your jewellery knowledge or trying to discover some untold secrets in the jewellery business? Uncut diamonds are not something that are often talked about. This article breaks down everything you need to know about uncut diamonds and how you can make a smart investment when picking out gorgeous stone jewellery.

Uncut Diamond Jewellery explained?

An uncut diamond, as suggested by the name, is a diamond in its most natural form. Prior to any shaping to enhance proportion, symmetry and polish involved in diamond cutting, an uncut diamond is a raw diamond that is completely virgin and free from human manipulation.

What is an uncut Diamond worth and why are Diamonds cut?

Apart from their quirky edge, there is not a whole lot of value in purchasing raw diamonds. Uncut diamonds are typically worth less than traditionally cut diamonds as their unpolished, rough edges hinder how well light is refracted. This reduces their sparkle and brilliance, thus reducing their market value.

What does a raw uncut Diamond look like?

An uncut diamond is often bumpy and dull with no real structure. An acquired taste, uncut diamond rings provide a uniqueness and level of beauty some like to hold with others even looking for a rough diamond ring to mark their love.

Diamond Cut Breakdown

To create beautiful diamonds that are worth thousands, diamond cutters have the difficult job of trying to create finished products which align in proportion, symmetry and polish. Make no mistake, while this is easy to decide in theory, cutting diamonds is a challenge where compromises often have to be made. Compromising factors such as diamond weight, to create the right proportions and symmetry, or proportions and symmetry to avoid cutting further diamond and reducing weight.

In a similar way to natural diamonds, poorly cut diamonds can also refract light badly, resulting in little to no sparkle and less spread for your carat weight. To identify how you can be savvy and well informed when choosing your own diamond jewellery, here is everything you need to know about cuts.

Developed in the 1940’s to 1950’s by the Gemological Institute of America (GIA), cut grades were developed to allow independent labs to identify a diamond’s clarity, colour and structure. A prime example of a predetermined cut grade is a brilliant cut diamond which will have 57 or 58 facets accurately cut and defined. While miniature, this provides a system to govern how well a diamond will sparkle.

While they offer a significant discount in price, poorly cut diamonds lack luster and you will be paying for a diamond without any sparkle. To ensure that you have an effective diamond that is worthwhile, we always suggest purchasing a diamond with an “excellent” to “good” cut. However, if you are still interested in purchasing a diamond of a lower cut, we suggest taking a look in person under various lighting conditions to avoid any disappointment.

Website, other related websites and blogs created as a scratch base pilot project for merging and evolving to something better and highly valuable.