Hey HN — I kept seeing developers describe the same frustration: the commit-push-wait-read-logs cycle when debugging CI pipelines. So I built PipeStep.PipeStep parses your GitHub Actions YAML, spins up the right Docker container, and gives you a step-through debugger for your run: shell commands.You can:
1. Pause before each step and inspect the container state.
2. Shell into the running container mid-pipeline (press I). 
3. Set breakpoints on specific steps (press B).
4. Retry failed steps or skip past others.It deliberately does not try to replicate the full GitHub Actions runtime — no secrets, no matrix builds, no uses: action execution. For full local workflow runs, use act. PipeStep is for when things break and you need to figure out why without pushing 10 more commits. Think of it as gdb for your CI pipeline rather than a local GitHub runner.pip install pipestep (v0.1.2) · Python 3.11+ · MIT · Requires DockerWould love feedback, especially from people who&#x27;ve hit the same pain point. Known limitations are documented in the README + have some issues in there that I&#x27;d love eyeballs on!

Show HN: PipeStep – Step-through debugger for GitHub Actions workflows

I&#x27;m a VP of Engineering with 20 years in the field. I&#x27;ve been thinking deeply on why AI is breaking every engineering practice, 
and it led me to the conclusion that the Agile Manifesto&#x27;s values need 
updating.The core argument: AI made producing software cheap, but understanding it 
is still expensive. The Manifesto optimizes for the former. This addendum 
shifts the emphasis toward the latter.Four updated values, three refined principles, with reasoning for each. 
Happy to discuss and defend any of it.

Show HN: An addendum to the Agile Manifesto for the AI era

Hey HN, I made a site where you can draw on street-level panoramas. Your drawings persist and other people can see them in real time.Strokes get projected onto the 3D panorama so they wrap around buildings and follow the geometry, not just a flat overlay. Uses WebGL2 for rendering, Mapillary for the street imagery.The idea is for it to become a global canvas, anyone can leave a mark anywhere and others stumble onto it.

Show HN: I Was Here – Draw on street view, others can find your drawings

I was frustrated with photo sharing sites. Apple&#x27;s iCloud shared albums take 20+ seconds to load, and everything else comes with ads, cumbersome UIs, or social media distractions. I just want to share photos with friends and family: fast, mobile-friendly, distraction-free.So I built DD Photos. You export photos from whatever you already use (Lightroom, Apple Photos, etc.) into folders, run `photogen` (a Go CLI) to resize them to WebP and generate JSON indexes, then deploy the SvelteKit static site anywhere that serves files. Apache, S3, whatever. No server-side code, no database.Built over several weeks with heavy use of Claude Code, which I found genuinely useful for this kind of full-stack project spanning Go, SvelteKit&#x2F;TypeScript, Apache config, Docker, and Playwright tests. Happy to discuss that experience too.Live example: <a href="https:&#x2F;&#x2F;photos.donohoe.info" rel="nofollow">https:&#x2F;&#x2F;photos.donohoe.info</a>
Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;dougdonohoe&#x2F;ddphotos" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;dougdonohoe&#x2F;ddphotos</a>

Show HN: DD Photos – open-source photo album site generator (Go and SvelteKit)

Hi HN - I&#x27;m Ryan a product designer who codes, and I built Mesa. Current IDEs feel wrong for the type of development being done now - the focus is still on files.Mesa puts the focus on the full workflow: your agent, terminal, browser, and files all live as equal nodes on a canvas with full multiplayer support. (think figma but for code)I was tired of the overhead of switching windows, tabs, and terminals across multiple projects. Inspired by TouchDesigner and Factorio, I wanted something more fluid and visual. Been using it as a total replacement for Cursor at work every day now. Being able to see multiple repos at once and control agents on each without navigating windows has freed up my headspace and increased productivity.It&#x27;s free to try — would love to know what you think!

Show HN: Mesa – A collaborative canvas IDE built for agent-first development

Show HN: Argus – AI agent that investigates infra anomalies and proposes fixes

Post-Opus 4.6, LLMs feel much better at using bash, code, local files, and tools.So I kept coming back to a simple question: if a model can use a computer reasonably well, why can’t I just give it my broker account, a strategy, and let it trade?My conclusion is that the blocker is not model capability in the abstract. It is the system around the model.A raw LLM breaks on a few practical things almost immediately:• no persistent operating memory across sessions
• no trustworthy record of what it did and why
• no hard approval boundary before money moves
• no cheap always-on monitoring if every check requires an LLM call
• no reliable enforcement of limits, permissions, or workflow rules unless that lives outside the modelSo the problem is not really “can the model call a broker API?” The problem is that trading needs a harness.My friend and I built one for this called Vibe Trade. It is open source, MIT licensed, and currently runs locally on your machine connected to Dhan.The basic design is:1. Immutable trade journal
Every action is logged at decision time with timestamp, reasoning, and observed signals. The agent cannot rewrite its own history after the fact.2. Hard approval gate
Before any order is placed, the system generates a structured approval request. Execution is blocked until the user approves. This is enforced in code, not left to the model’s discretion.3. Event loop outside the LLM
Market watching is handled in plain JS on a timer. Price checks, time rules, and indicator thresholds run every 30 seconds without invoking the model. The LLM only wakes up when something needs reasoning.4. Playbooks &#x2F; skill files
Strategies live in markdown documents that get loaded as operating context on each decision. Example: “replicate the Nifty Defense Index and rebalance weekly.” This gives the agent a stable workflow definition instead of relying on chat history.The first use case that made this feel real to me was very unglamorous: portfolio rebalancing.I used to make Smallcase-style index replication portfolios and then forget to rebalance them on time. With this setup, I can define the strategy once, let the non-LLM layer monitor for conditions, and have the agent prepare actions for approval. That was the first point where it stopped feeling like a demo and started feeling useful.A few caveats:
• UI is still weak; it is mostly a chat interface right now
• Dhan only for now
• local install only for now
• requires Node.js and an Anthropic API keyRepo: github.com&#x2F;vibetrade-ai&#x2F;vibe-tradeI’m posting this mainly because I think more people will try building “LLM as operator” systems now that tool use is better, and finance makes the failure modes very obvious.The questions I’m interested in are:• What other harness components are missing for something like this?• Would you trust a local system like this more than a hosted one, or less?• What repeatable financial workflows would you automate first?

Show HN: VibeTrade – Trading Harness for Claude

I&#x27;m Greg, co-founder of VaultLeap.Built this for founders who can&#x27;t get a US bank account. USD&#x2F;EUR&#x2F;MXN accounts with real ACH routing numbers and we have Visa cards coming soon.If you&#x27;ve been cut off from Mercury or similar recently, DM me — happy to help some founders out.

Show HN: VaultLeap – USD accounts for founders outside the US

I built a local-first UI that adds two reasoning architectures on top of small models like Qwen, Llama and Mistral: a sequential Thinking Pipeline (Plan → Execute → Critique) and a parallel Agent Council where multiple expert models debate in parallel and a Judge synthesizes the best answer. No API keys, zero .env setup — just pip install multimind. Benchmark on GSM8K shows measurable accuracy gains vs. single-model inference.

Show HN: Run an Agent Council of LLMs that debate and synthesize answers

Show HN: Calyx – Ghostty-Based macOS Terminal with Liquid Glass UI

Show HN: Free audiobooks with synchronized text for language learning

I built Chronoscope, a project to explore the world through time.I&#x27;ve been wanting to do this for a while, after being inspired by Ollie Bye&#x27;s &quot;History of the World&quot; video several years ago.I&#x27;m not the first person to have done this - resources like OpenHistoricalMaps are amazing.But, I noticed there were a few disparate datasets &#x2F; academic databases online, so I combined them together as best as I could (I&#x27;ve linked all sources in the app). To make it more interesting, I also included:- Notable events from the time period (geolocated where possible), sourced from wikidata- Ancient cities + their original names- Empire hierarchies for colonial empires like the British EmpireYou can jump across time and use shuffle to explore some fascinating corners of history.Would love any feedback, especially from people who like maps, timelines, and weird historical rabbit holes. Also please report any data issues if you find them (it&#x27;s all using publicly collated data, so there will be plenty).Happy to publish code &#x2F; data on GH if there&#x27;s interest!

Show HN: I built Chronoscope, because Google Maps won't let you visit 3400 BCE

Native Desktop is a toolkit for building native desktop applications using modern web technologies without dealing with the usual complexity of desktop tooling. It focuses on providing a simple developer experience where you can scaffold, build, and distribute desktop apps using familiar workflows and a modular package ecosystem. Instead of forcing developers to manage complicated native environments, Native Desktop provides a CLI and a set of packages that handle the heavy lifting while keeping projects flexible and maintainable. The goal is to let developers move from an idea to a working desktop application quickly while still having full control over architecture and distribution. The project is designed for developers who already build with modern web stacks and want a straightforward way to turn those applications into desktop software without reinventing the entire toolchain.

Show HN: Cloud to Desktop in the Fastest Way

Last year, I went to a game at a stadium I had never visited (Arrowhead). Ended up baking in the sun for the entire second half. Afterwards, was surprised I couldn&#x27;t find a tool that told me where the sun would actually be during games.So for WC2026 I built SeatSun: a free tool (no sign up needed) that calculates sun&#x2F;shade exposure for every section at the World Cup 2026 stadiums depending on date and match time.The technical core: solar azimuth and altitude calculations (based on Meeus astronomical algorithms, ~0.3 degree accuracy) tied to each stadium&#x27;s orientation and GPS coordinates. For each open-air stadium I mapped the seating sections to compass bearings, then render an interactive overlay showing which sections are in sun vs shade at the exact match time. The sun position shifts meaningfully across a single afternoon -- a 3pm kickoff looks very different from a 6pm kickoff at the same stadium.Covers 16 stadiums: 13 open-air with interactive sun maps, 3 indoor (AT&amp;T, Mercedes-Benz, NRG) with appropriate notes. You can set any date and kickoff time to see the exposure for your specific game.Would love feedback on the accuracy, especially from anyone who has been inside these stadiums and can sanity-check the section orientations. A few of the venues were harder to calibrate than others, and I haven’t yet made adjustments other than notes for partially covered stadiums (eg Hard Rock).

Show HN: I calculated sun/shade exposure for every seat at World Cup stadiums

Show HN: A desktop app for managing Claude Code sessions

Ash is a macOS sandbox that restricts AI coding agents. It limits access to files, networks, processes, IO devices, and environment variables. You can use Ash with any CLI coding agent by wrapping it in a single command: `ash run -- &lt;agent&gt;`. I typically use it with Claude to stay safe while avoiding repetitive prompts: `ash run -- claude --dangerously-skip-permissions`.Ash restricts resources via the Endpoint Security and Network Extension frameworks. These frameworks are significantly more powerful than the sandbox-exec tool.Each session is driven by a policy file. Any out-of-policy action is denied by default. You can audit denials in the GUI app, which lets you view out-of-policy actions and retroactively add them to your policy file.Ash also comes with tools for building policies. You can use an &quot;observation session&quot; to watch the typical behavior of a coding agent and capture that behavior in a policy file for future sandbox sessions. Linting, formatting, and rule merging are all built into the Ash CLI to keep your policy files concise and maintainable.Download Ash at <a href="https:&#x2F;&#x2F;ashell.dev" rel="nofollow">https:&#x2F;&#x2F;ashell.dev</a>

Show HN: Ash, an Agent Sandbox for Mac

Hello HN, we&#x27;re Jeet and Husain from Modulus (<a href="https:&#x2F;&#x2F;modulus.so" rel="nofollow">https:&#x2F;&#x2F;modulus.so</a>) - a desktop app that lets you run multiple coding agents with shared project memory.
We built it to solve two problems we kept running into:- Cross-repo context is broken. When working across multiple repositories, agents don&#x27;t understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo.- Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again.Modulus shares memory across agents and repositories so they can understand your entire system.It&#x27;s an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents.Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus.Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context.If you wanna see a quick demo before trying out, here is our launch post - <a href="https:&#x2F;&#x2F;x.com&#x2F;subhajitsh&#x2F;status&#x2F;2024202076293841208" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;subhajitsh&#x2F;status&#x2F;2024202076293841208</a>We&#x27;d greatly appreciate any feedback you have and hope you get the chance to try out Modulus.

Show HN: Modulus – Cross-repository knowledge orchestration for coding agents

I’ve been building a crowd-sourced AI detection benchmark. Two responses to the same prompt — one from a real human (pre-2022, provably pre prevalence of AI slop on the internet), one generated by AI. You pick the slop. Three wrong and you’re out.The dataset: 16K human posts from Reddit, Hacker News, and Yelp, each paired with AI generations from 6 models across two providers (Anthropic and OpenAI) at three capability tiers. Same prompt, length-matched, no adversarial coaching — just the model’s natural voice with platform context.
Every vote is logged with model, tier, source, response time, and position.Early findings from testing: Reddit posts are easy to spot (humans are too casual for AI to mimic), HN is significantly harder.I&#x27;ll be releasing the full dataset on HuggingFace and I&#x27;ll publish a paper if I can get enough data via this crowdsourced study.If you play the HN-only mode, you’re helping calibrate how detectable AI is on here specifically.Would love feedback on the pairs — are any trivially obvious? Are some genuinely hard?

Show HN: Slop or not – can you tell AI writing from human in everyday contexts?

Relay Architecture is a new way to run AI coding agents. Instead of one agent executing your entire plan until it degrades, you run a relay. Each fresh agent reads the full plan, picks up a minimal handoff from the previous one, works at peak capacity, and passes the baton forward. The team covers a distance no single agent could sustain.The problem it exists to solve: LLMs degrade as context fills up — this is measured, not anecdotal (Chroma Research). Every AI coding tool today runs a single agent start to finish. By phase 4 of your plan, half the context window is spent remembering what was already done. I was restarting sessions by hand, copy-pasting progress notes just to keep quality up. That&#x27;s not autonomous development. That&#x27;s babysitting.
The architecture has four components: the Master Plan (your PRD, read fresh by every agent — never accumulates in context), the Baton (a hard-capped 40-line handoff note — the constraint is intentional, a bloated handoff recreates context rot in the next agent), the Signals (trigger phrases agents emit so the orchestrator dispatches without understanding the code), and the Context Budget (real-time token tracking, automatic handoff at threshold).Tarvos is the reference implementation for Claude Code. Each session runs in its own git worktree. Accept merges to your branch, reject discards cleanly.Open source, MIT. Works today, rough edges exist.

Show HN: Tarvos – Relay Architecture for infinitely building with coding agents

Show HN: Stratum – SQL that branches and beats DuckDB on 35/46 1T benchmarks

Do we really need another agent orchestrator? Probably not. But I couldn&#x27;t find one that matched how I actually work with coding agent CLIs, so I built my own.Codelegate is a desktop app (Tauri 2 + React + xterm.js) that organizes agent sessions into a keyboard-first workspace. I built it to solve a few specific frustrations:1. I want to navigate everything with both hands on the keyboard. Sessions switch with `Alt+1..9`, panes with `Alt+A&#x2F;G&#x2F;T`. No mouse required.
2. I work on the same repo in parallel using Git worktrees. Codelegate has a built-in worktree flow: create an isolated branch per agent, auto-cleanup on session end.
3. I want to keep using my CLI tools (zellij, etc.) alongside agents, not replace them.
4. I need it on both macOS and Linux.Each session gives you three panes: Agent, Terminal, and Git. The Git pane handles diff review with syntax highlighting, bulk stage&#x2F;unstage, commit, and amend. Sessions are grouped by repository in the sidebar.Currently supports Claude Code and Codex CLI, but anything that runs in a shell can work.This is v1.0.0 and it only covers the agent CLIs and features I use the most. It&#x27;s licensed under GPLv3, so it&#x27;s meant to be forked and shaped into your own workflow.Hope you enjoy using it or making it your own!

Show HN: Codelegate, keyboard-driven coding agent orchestrator GUI for Mac/Linux

We&#x27;ve indexed 5M+ GitHub users and built a ranking system that goes beyond follower counts.
The idea started from frustration: GitHub is terrible for discovery. You can&#x27;t answer &quot;who are the best Python developers in Berlin?&quot; or &quot;who identified transformer-based models before they blew up?&quot; without scraping everything yourself. So we did.What we built:
CodeRank score - a composite reputation signal across contributions, repository impact, and community influence
Tastemaker score - did you star repos at 50 stars that now have 50,000? We track that
Comparison Builder - allows users to build comparison graphics to compare devs, repos, orgs, etc.
Sharable Profile Graphics - share your scores and flex on your coworkers or the community at largeSome things we found interesting:
Most-followed ≠ most influential. The correlation between follower count and tastemaker score is surprisingly weak. There&#x27;s a whole tier of developers who consistently find projects weeks and months before they trend, with almost no public following.Location data on GitHub is a disaster. We spent an embarrassing amount of time on normalization and it&#x27;s still not anywhere near perfect.Try it: <a href="https:&#x2F;&#x2F;coderank.me&#x2F;" rel="nofollow">https:&#x2F;&#x2F;coderank.me&#x2F;</a>If your profile doesn&#x27;t have a score, signing in will trigger scoring for your account.Curious what the HN crowd thinks about the ranking methodology, happy to get into the weeds on any of it.

Show HN: Every Developer in the World, Ranked

Hey HN, I&#x27;m Shubh, Co-Founder of Raccoon AI.Raccoon AI is like having something between Claude Code and Cursor in the web.The agent has its own computer with a terminal, browser, and internet, and it is built with the right balance of collaboration and autonomy.You can talk to it mid-task, send it more files while it&#x27;s still running, or just let it go and come back to a finished result.It&#x27;s the kind of product where you open it to try one thing and end up spending two hours because you keep thinking of more things to throw at it.The thing that most people get excited about is that sessions chain across completely unrelated task types. You can go from market research (real citations, generated charts) to raw data analysis (dump your db, ask questions) to a full interactive app, all in one conversation sharing the same context.It has unlimited context through auto summarization, which is really good with Ace Max.It connects to Gmail, GitHub, Google Drive, Notion, Outlook, and 40+ other tools. You can add your own via custom MCP servers.Raccoon AI is built on top of our own agents SDK, ACE, which hit SOTA on GAIA benchmark with a score of 92.67.A bit of background: We&#x27;re a team of 3, and we started about 1.5 years ago to build the best possible browser agent to ever exist, after a couple of pivots we arrived at this and have been constantly shipping and growing since October.Happy to go deep on the architecture or talk about the limitations and excited about the feedback.Site: <a href="https:&#x2F;&#x2F;raccoonai.tech" rel="nofollow">https:&#x2F;&#x2F;raccoonai.tech</a>

Show HN: Raccoon AI – Collaborative AI Agent for Anything

Show HN: A playable version of the Claude Code Terraform destroy incident

People around me started repeating stuff from various psyop campaigns on TikTok or other social media they consume.Especially when living in Baltics it&#x27;s basically 24&#x2F;7 fearmongering here from anywhere, either it&#x27;s constant russian disinfo targeted campaigns via their chains of locals or social media campaings or some bloggers chasing hype on clickbait posts, so it was driving me mad, and it is distracting and annoying when someone from your closest ones got hooked on one of these posts and I was wasting time to explain why it was a bs.So I took my slopmachine and some manually tweaking here and there and made this dashboard. Main metric is basically a daily 0-100 threat score, which are just weighted sums and thresholds - no ML yet.

Show HN: Baltic security monitor from public data sources

Show HN: Xr – Ripgrep for Binary Xrefs

Show HN: Hyper – Voice Notes for Whiteboarding Sessions

Show HN: Claude Code Token Elo

Built this because I wanted a better browser runtime for Openclaw, which can run on any server no only on Mac mini, emm. 
When it needs me to login or perform some operations, I can simply use noVNC to operate, and then leave everything else to it.

Show HN: Verge Browser a self-hosted isolated browser sandbox for AI agents

Unlike Terraform and Pulumi, Autoschematic is built around a bidirectional (push-pull) state model. This means that it can resolve state drift by &quot;pulling&quot; or &quot;pushing&quot; (applying). This makes it a much better fit for certain use-cases where configuration drift is more common, like Snowflake. It also means you can import your existing infra automatically.