Codacy

Code Quality and Security for AI-Accelerated Coding.

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[Codacy @codacy]

@jaimefjorge @lucaronin where would you position @tolariamd? :)

[Jaime Jorge @jaimefjorge]

Steal this AI maturity scale to guide your team toward loop engineering. 👇 (sound on 🔊)

Most teams think they're at one "stage" of AI adoption. They're not. They're at all of them at once.

We spent the last few quarters talking to hundreds of software teams about how they really use AI and that surprised us.

Most people picture AI adoption as a ladder: autocomplete → prompting agents → "loop engineering," where agents run scheduled tasks, wired into your systems, operating on their own.

But the teams we talk to are doing all three at once. Autocomplete in one repo. Agents shipping features in another. Already running loops against their own infra.

What's consistent is the direction: more autonomy for agents, hand off the recurring and mechanical work, let people move up to the part that still needs a human.

Most teams are further down that road than they realize.

More autonomy brings more entropy and runaway cost if you scale agents uncontrollably (we've watched companies do exactly that). Plus real change, like reworking your Git flows to handle looping agents.

I pulled the best insights from those conversations into the video. Watch it and you'll probably spot where your team sits.

[Codacy @codacy]

Verity is now live in public beta.

A local, adversarial review layer built for coding agents.

It pairs deterministic checks with an independent model, to catch and repair security, quality, and intent gaps after every agent run. Every decision compounds into a markdown knowledge base, so each session starts smarter than the last.

Plus, you get live cost visibility across all your agents.

It’s free while in beta. Give it a try today and send us your feedback:

npm install -g @codacy/verity-cli && verity init

Learn more: https://t.co/82zkoERwPM

[Jaime Jorge @jaimefjorge]

Today we’re launching Verity.md in public beta. If you’re working with coding agents, we built this for you.

As AI writes more code, developers and engeneering teams lose visibility into quality, context, and cost.

Verity adds:
→ Gates (AI + deterministic reviews)
→ Memory (persistent repo knowledge)
→ Cost control (token & spend tracking)

Free. Install today: https://t.co/Qbe4iN9oYG Would love to hear any feedback from devs out there.

[Codacy @codacy]

"Are you getting it?"

Spoiler: Tomorrow you will.

[Jaime Jorge @jaimefjorge]

Maybe someone can help me: what is effectively the difference between using claude code's ultracode (which you can define a goal and it uses workflows with many agents etc) and loop engineering? isn't it effectively the same?

[Codacy @codacy]

Across our customers' repos, we found several instances where agent instruction files (like Claude.md) contain a hardcoded secret. Be sure to your treat your agent instructions like you’d treat your code.

Learn more about how Codacy scans your agent files https://t.co/ItDyonhM7N

s/o @simonkim_nft

[Codacy @codacy]

PR queues are growing faster than review capacity, but rubber-stamp approvals to clear the backlog isn't the answer. This article covers how to keep PRs moving without lowering your standards.

Read full piece https://t.co/Pm46f6zOYu

[Codacy @codacy]

Fable 5's own internal self-verification is strictly probabilistic (it writes its own tests, reflects on reasoning, and uses vision). Because Fable 5 is essentially “grading its own homework,” it relies on the same neural weights and has the same blind spots that produced the code in the first place.

If this sounds less than ideal, it's because it is. Check out our recent article on why coding agents need independent quality gates:

https://t.co/Gr9is6UytQ

[Codacy @codacy]

As our customers' agents got busier, so did our SREs.

Over the last few months our infrastructure team restructured the database behind code analysis, cut worker memory consumption in half, and tightened how we allocate compute.

The result is the average duration of a code analysis is stable even as the analyses keep climbing.

Come at us 🤖

[Codacy @codacy]

This is for the engineers who have to defend paying down tech debt against feature work.

We put together a guide on what's actually worth tracking, how to measure it without it turning into an opinion battle in sprint planning, and how to keep the practice alive past the first month.

Read full article https://t.co/pUf6gBcZpc

[Codacy @codacy]

Starting June 1, GitHub Copilot code review is no longer included in your subscription. It now draws from the same token pool as chat, agents, and CLI and separately consumes GitHub Actions minutes on private repos. Two line items where there used to be none.

Our CTO Kendrick wrote up a piece what you need to know: https://t.co/p1RbmcMx3J

[JNation 🇵🇹 May 26 & 27 - 2026 @JNationConf]

Luis Ventura is now discussing AI, production failures and engineering workflows at #JNation.

#AI #DevOps #Coimbra

[Codacy @codacy]

Codacy now integrates AgentLinter, a tool with 100+ rules that checks your agent config files for security issues, unclear instructions, and missing guardrails. Credit to @simonkim_nft and @subinium for building the tool.

Read the full update and capabilities. https://t.co/ItDyonhM7N

[Codacy @codacy]

Generation → verification → correction. The loop AI-heavy codebases need: When an agent ships a change, an independent verification layer runs deterministic checks and returns pass/fail with reasons, and on failure the agent retries against structured feedback instead of guessing.

Skip that middle step and errors compound silently across commits.

Read article: https://t.co/Gr9is6V6jo

[Codacy @codacy]

For the engineering teams quietly wondering what AI models and tools have piled up in their codebase: AI Inventory maps every model, SDK, API key, and MCP server used on the codebase, down to the file and line.

Start free https://t.co/qwjD8cWgpK

[Codacy @codacy]

"Engineering leaders know their teams are using AI tools, they just can't tell you exactly which ones, where, or how they're configured. We want every engineering organization to have that answer before anyone comes asking for it." @jaimefjorge https://t.co/bwuyELyLpB

[Codacy @codacy]

AI Inventory is live.

It scans your connected repos and shows you every AI model, library, API key and endpoint used across your codebase. No new setup, no agents, no surveys. Now available for all paid users and new trials.

→ Read the full update https://t.co/3vRnEmwXro

[Codacy @codacy]

Now that we've 100%ed code analysis, we are proud to embark on a new journey.

Today, we’re applying our linting and scanning expertise to the tangible world.

Introducing Codacy Lint ‘n’ Scan Hardware: A curated line of premium linters, scanners, and detection tools designed to solve real-world problems with the same precision we brought to your codebase.

Check out our new collection: https://t.co/mAo4ZiZnln

[Aman @Amank1412]

SaaS is dead.

We replaced $750/month in SaaS with $4,570 in LLM tokens.

Now the team spends half their time debugging vibe-coded chaos instead of shipping.

But hey… we “own the stack.”