I manage two engineering teams, 12 engineers in total. Over the past few months, complaints about PR reviews have been coming up more and more. Too many PRs, reviews taking too long, and people getting blocked waiting for someone to take a look.

It's become one of our biggest bottlenecks to shipping.

We also recently had a regression where an improvement to one of our core features ended up breaking three other important functionalities. We spent the next two weeks fixing regressions.

That got me thinking quite a bit about how we review code. We need to review faster, but clearly we can't afford to be less thorough either.

I've been building an open source tool called Alden to try to help with this, and I've used Claude Code quite extensively to build it.

Claude helped me design the UI, built most of the website, wrote the documentation and helped implement the deterministic pipeline that runs the checks. I also used it for QA, which was probably one of the more interesting parts of the process. I had Claude draft test cases from my acceptance criteria, then open a browser and test the same scenarios I was testing manually, in parallel. Basically a second pair of hands doing QA alongside me.

I recently added a Claude Code plugin so I could use Alden directly in the same environment I'd been building it with.

The idea is fairly simple. Rather than having Claude review a diff and dump a bunch of comments, Alden gives it more context about the change and helps you figure out where to spend your attention.

For example:

• /alden:queue shows the PRs waiting for your review and ranks them.
• /alden:review 2 prepares a brief on the second PR in your queue, including potential issues and callers of changed functions outside the diff. Claude can then investigate further.
• /alden:remember "Money is in cents, never floats" saves context that gets included in future reviews of that repo.

You can also ask Claude to review a specific PR or tell you what's waiting on you.

One design decision I made was to keep submitting reviews separate from the agent. Claude can draft comments in Alden, but the plugin doesn't give it a tool to publish them. You review and submit them yourself.

To be clear, this isn't a security boundary. Claude Code may have access to other tools or credentials that could let it interact with GitHub. I'm just deliberately not making publishing part of the plugin's workflow.

There's also a feedback loop where you can tell Alden which findings were useful so it can adjust what it prioritises over time.

The plugin uses Claude Code's existing model, so you don't need a separate model API key for it.

It's open source and free.

GitHub: https://github.com/getalden/claude-plugin

Docs: https://getalden.dev/docs/claude-code

I'm curious how others are dealing with this. Have PR reviews become a bottleneck for your teams too? And has AI actually helped you review faster without compromising quality?

**Setup**

- React Native 0.87.1

- Xcode 27.1

- iOS Simulator (iPhone 18 Pro, iOS 27.0)

I've hit this a lot: the app dies on iOS, the JS console is quiet, and it's not obvious where to start. Most of the time the answer is on the native side, so here's the process I use. I call it RIFT.

**R – Reproduce**

Run the app from Xcode (not just from the terminal) and trigger the crash. Add a breakpoint near where you think it happens so you can pause instead of watching the app die.

**I – Investigate**

When it stops or crashes, look at the exception message and the backtrace in the left panel. In my example, a native module method called from JS threw an uncaught NSException, and the console printed the reason. Then in LLDB:

- `bt` – print the backtrace

- `frame select <n>` – jump to a specific frame

- `po` / `p` / `expression` – inspect values at that point

**F – Fix**

Change the actual cause in the native code (in my case, the method wasn't validating its input), not just the symptom.

**T – Test**

Re-run the exact same steps and confirm the crash is gone. Skipping this step is how "fixed" bugs come back.

I recorded a ~22 min live walkthrough of this on a real crash if it's useful to anyone: https://youtu.be/oGq0yZ3z9ks

Happy to answer questions. What's your usual first step when a crash shows no JS error?

Like a lot of you, I routinely paste logs, stack traces and .env snippets into AI tools. Every so often a real key slips through, so I built LeakGoblin.

It's a small TypeScript CLI that works on stdin or a directory:

bash

npx leakgoblin scan --stdin < prompt.txt
cat prompt.txt | leakgoblin sanitize --stdin > safe.txt
leakgoblin scan . --format json

What it does:

• Format-specific signatures (AWS, GitHub, Stripe, Slack, Google, OpenAI-style keys, private key blocks, JWTs, connection strings) plus a clearly labeled low-confidence entropy heuristic
• Output is always masked, and JSON output strips raw values
• Runs fully offline, with no telemetry
• CI-friendly exit codes (0 clean / 1 found / 2 error)
• Config file for custom patterns, thresholds and an allowlist

It is a best-effort filter, not a guarantee. It will miss unusual formats, so review the output before sharing.

npm: https://www.npmjs.com/package/leakgoblin GitHub: https://github.com/lovixcloud/leakgoblin

I'd love feedback on false positives and missed formats in particular. Send me a synthetic example and I'll add a rule.

Built this to solve a specific annoyance: when something crashes in production, the log is the last place anyone wants to be at 3am. Watcher runs in the background, tails your logs, and when it sees a crash, explains the root cause using a small LLM running locally through Ollama, no cloud calls, nothing leaves the machine.

What it does:

• Watches a file, stdin, or a Docker container's logs
• Deduplicates repeated crashes into one incident instead of alerting per occurrence — a crash loop produces one incident with a live count, not a flood
• Reports a structured explanation (likely cause, suggested fix, confidence) to the terminal, as JSON, or over a webhook
• Flags its own confidence as low when the evidence it cited doesn't actually hold up in the excerpt it was given, rather than guessing with false certainty

Two real examples from testing: killed a database connection, and it named the cause correctly with full confidence, because the evidence actually held up. Separately, pulled a required env var, it still named the right cause, but flagged its own confidence as low because part of what it cited wasn't in the excerpt. That second one is the part I actually care about with a model this small — it'll be wrong sometimes, the real question is whether it knows when to hedge.

Known rough edges: onboarding and the TUI (watcher attach) both work but need polish, Docker Compose packaging and in-repo evaluation scoring aren't done, and I'm still chasing down why inference latency is higher than it should be on constrained hardware (~2 min per explanation right now on a 2 vCPU / 4GB box with a 1.5B model).

MIT licensed. Feedback, issues, and PRs genuinely welcome; a few issues are tagged for first-time contributors if you want somewhere to start.

I built a VS Code extension for people who want to manage their GitHub docs without leaving the current workspace.

GitHub README lets you browse your repositories, open any README, edit it, preview the rendered Markdown, and push the changes back to GitHub all without leaving the current workspace.

Basically:
VS Code → pick a repo → edit README → preview → commit → done.

No jumping between GitHub, cloning repos, or switching windows just to change a README

#- https://marketplace.visualstudio.com/items?itemName=itshakemd.github-readme

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The github default pr dashboard not very useful or user friendly, however, now I coding work are mostly done by agent with high volume of prs. I really need a powerful pr dashboard to monitor/manager prs from agent. So I build this tool with codex and claude designer. It supports almost all actions at pr level, detailed code review is not the goal for this tool. Please try it and leave feedback.

Installation: `gh extension shiqicao/gh-pull`.

Source code, https://github.com/shiqicao/gh-pull

One feature in my app turned out to be more fun than expected. An agent running in Lattice can open helper agents, each in its own window with its own git worktree so they never edit the same files. The main agent can wait for them, read what's on their screens and send them instructions, and I can watch the whole thing happen and step in whenever I want.

Each helper still asks me for approvals in its own window, so nothing runs behind my back. It's been great for splitting up bigger tasks like "fix these three bugs" into parallel work.

Lattice is a free Mac app in public beta. Curious whether anyone else has tried this kind of setup and how it went for you.

All i want is some honest feedback, what to improve and what to add, download it here: uselattice.site

Hey everyone!

I've been working on Boiler, a command-line tool that helps developers create, manage, and reuse project templates. I just released its first beta version, `v0.0.1-beta`.

The idea is simple: instead of repeatedly setting up the same project structure, you can save it as a template and reuse it whenever you start a new project.

What Boiler can do:

* Create and manage project templates.

* Add files and directories to existing templates.

* Set a current template for easier access.

* Initialize new projects from saved templates.

* Manage everything through a CLI.

I've also packaged it as a Windows executable with an installer.

This is my first beta release, so I'd appreciate honest feedback! I'd especially like to hear what features you'd find useful and what could be improved.

GitHub: https://github.com/MrPsyghost/Boiler

Download: https://github.com/MrPsyghost/Boiler/releases

Thanks for checking it out!

Sharing SessionSift, a tool I vibe-coded and then tested and used myself to review AI coding sessions. I wanted to compare long conversations with separate sessions, inspect prompt patterns, and improve how I direct coding agents.

It's a free, local-first way to inspect historical Codex and Claude Code conversations. Features include session browsing, token and tool activity, comparisons between sessions, explicit imports of ChatGPT/Claude exports, and 10 read-only local MCP tools.

No account or cloud backend is required to run the dashboard. Connected MCP clients may still send tool output to their model providers. Screenshots and installation instructions are in the repo.

Current release: native macOS Intel and Linux AMD64, with source/Docker options. macOS binaries are unsigned and unnotarized. License: PolyForm Noncommercial 1.0.0 (source-available, not OSI open source; commercial use isn't licensed).

https://github.com/LanceSandino/sessionsift

Would welcome technical feedback on what makes session-history tools useful versus just another log viewer.

I switch between Claude Code and Codex depending on the task, and I got tired of re-explaining project state every time I switched.

So I built AskOnce — a free Windows app that captures a brief from one tool (last message, files changed, test results, next steps, open questions) and hands it to the other when you start a new chat there.

Everything stays on your machine. No server, no account, no telemetry. Briefs are secret-scanned and capped at ~1500 tokens so you're not burning context.

It's Windows 10/11 only for now. If anyone else is juggling both tools, would love to hear if this solves a real problem for you or if I'm the only one annoyed by this.

askonce.xyz/

Hooks fire before the agent reads a file, so it knows about your other repos without being asked. Indexes Docker images, Terraform modules, GitHub Actions, Helm charts and npm packages. Everything local, one SQLite file.

pip install blastradius-mcp

[github.com/harris-ahmad/blastradius-mcp](http://github.com/harris-ahmad/blastradius-mcp)

Hey everyone!

I inspect web stacks all the time to see what libraries and frameworks websites are running, but similar tools have gotten pretty bloated and full of trackers.

So I made TechReveal, a clean and lightweight alternative:

• Zero tracking: Runs 100% locally in your browser sandbox, zero telemetry, no accounts.
• Fast & light: Built with Manifest V3 so it won't freeze active tabs or hog RAM.
• Visual stack map: Connects the frontend, backend, and tools into an interactive diagram.
• Exact versions & tags: Shows library versions and category badges when you click any tech.
• Security alerts: Flags missing security response headers and data leaks.

It's completely free on the Chrome Web Store (works on Chrome, Brave, Edge, Vivaldi):
👉 TechReveal on Chrome Web Store

Would love some feedback from fellow devs or suggestions for tools you'd like it to detect next!

I built this project at a Vercel hackathon, and I found it quite useful: https://readmend.vercel.app/

I built Ringside at Knight AI+AV for a small annoyance in my coding workflow: keeping track of usage limits while moving between Claude and Codex.

It's a free, MIT-licensed Windows widget with an always-on-top view, a compact mode and a tray icon. The rings change colour at 75% and 90%, with optional notifications. It displays the usage and reset information available in local logs; it doesn't increase your allowance.

You can adjust its size and opacity, and it can hide over full-screen apps. Claude desktop reset times are estimates unless the optional Claude Code integration supplies them.

The current release supports 64-bit Windows 10 and 11. Source, installer and portable download: https://github.com/KNIGHT-AI-AV/ringside

The images show the actual UI with sample numbers. If you use these tools together, would you keep a full meter visible, use the compact view, or just want the notifications?

Standard B2B intent data platforms rarely work for developer tools and technical infrastructure products where traditional providers track broad web content consumption across business sites, which tells you nothing about an engineering team's architecture or active technical bottlenecks.

Developer intent doesn't show up in whitepaper downloads or marketing ad exchanges but where engineers actually solve problems: github issue, technical subreddits, infras hiring requirements, and docs page visits.

This is why technical GTM teams look at platforms like Scale Intelligence for developer intent where it continuously monitors technical channels like github, developer focused reddit communities, infra job postings and tech stack deprecations alongside public company data. It captures the specific technical challenges engineers are voicing, maps them back to the parent company, and identifies the engineering leaders managing those workflows.

For devtools, intent data is only actionable if it captures declared technical pain points directly from developer environments rather than generic business surge scores.

Hey everyone! I’m a solo builder working on ArchRivet. It connects to your AI assistant through MCP and helps review an architecture before you start building.

Your assistant explains the design. ArchRivet checks the details you provide, flags risks, and names what’s missing.

Here’s a simple example:

Your app needs prices less than five seconds old. During an outage, it serves saved prices.
ArchRivet asks: “How old can those saved prices get?”
If you answer “60 seconds,” it flags the conflict and shows why the review changed.

What it offers:

• Eight focused checks covering retries, caching, queues, transactions, and more.
• Traffic and storage calculations based on stated assumptions.
• Supporting references and specific follow-up questions.
• Local rules that need no GPU, with an optional 322M Laya model.

It’s an early preview, and reviews depend on the facts supplied. I’ve included demo GIFs, Codex setup instructions, and benchmark results with remaining mistakes.

GitHub, demos, and downloads

What’s one system-design mistake you’d want a tool like this to catch?

I'm building Genesys Code around a simple idea: developers shouldn't have to rely on just one AI coding model.

The platform brings multiple models together so users can switch between them and combine their capabilities in one workflow.

The goal is to make AI-assisted development more flexible and accessible.

https://genesyscode.vercel.app