🛠️ All DevTools

A Fed official is asking whether AI is becoming 'too big to fail'

Show HN: Capy – A Git-style platform for managing your team's secrets Hello HN!<p>We&#x27;re all spending more and more time making stuff with agents, but I&#x27;ve noticed that one of the things that is the most disconnected from my daily engineering workflow is dealing with secrets and credentials. It often involves a lot of click-ops, copying&#x2F;pasting, and collaboration, and none of the secrets management products out there have truly scratched that itch.<p>I built Capy to solve this. It&#x27;s a secrets manager whose entire frontend is a developer CLI, and I find it extremely ergonomic to use by hand. You also don&#x27;t need to leave the CLI to sign up for and use it! You can install and authenticate without leaving your terminal (or agent) session.<p>It also has very powerful version management with git-like branching and conflict resolution. You can push a version manifest to source control and have collaborators pull a specific version of the secrets at any point in time.<p>The platform itself is extremely secure. It encrypts your local .env files so they can&#x27;t be read directly. Upon pushing the values to the service, it encrypts them yet again with a service key. That way neither a compromised local machine nor a compromised service alone will result in a leakage.<p>BTW: Since it was released in April, I&#x27;ve been refining it and have evolved the focus a bit. Originally I was going for more TUI and TTY with guided wizards for doing things like deployments, rotations, and connecting services, but I realize that the real future is in making the product work EXTREMELY well with agents.<p>So the next evolution of is a more satisfying Agent ergonomic, and I have been working on something big next on that front.<p>Curious about your thoughts on the idea, execution, and would appreciate any and all feedback!

Muse Code and Muse Spark 1.2

Sula: A Gemini protocol server written in Scryer Prolog

Show HN: Spltty – a Markdown-based personal finance CLI built with Claude I built Spltty, a Ruby CLI for tracking shared expenses, custom splits, and settlements using plain Markdown files. The article explains how it evolved from a Claude-managed folder into a CLI

Microsoft's AI Sales Mostly Come from OpenAI, Disclosures Show <a href="https:&#x2F;&#x2F;www.bloomberg.com&#x2F;news&#x2F;articles&#x2F;2026-08-05&#x2F;microsoft-s-ai-sales-mostly-come-from-openai-disclosures-show?accessToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzb3VyY2UiOiJTdWJzY3JpYmVyR2lmdGVkQXJ0aWNsZSIsImlhdCI6MTc4NTk1NDMyMiwiZXhwIjoxNzg2NTU5MTIyLCJhcnRpY2xlSWQiOiJUSjlLT1VUOTZPU0gwMCIsImJjb25uZWN0SWQiOiI5NDU1RERFRjRCOUU0MDM3QTI5NkMyNEQyNjRENTFFMCJ9.Ul4Of4S6wIfB0K6bMz9t38wf2wj_xW_Ps692eR6PPs4" rel="nofollow">https:&#x2F;&#x2F;www.bloomberg.com&#x2F;news&#x2F;articles&#x2F;2026-08-05&#x2F;microsoft...</a>

Show HN: ClickBench Playground – a playground for 110 database systems I created it mostly for testing and exploration, but the main reason was that it became possible after previous work.

Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence (2025)

Phishers are hijacking legitimate cloud infrastructure

Show HN: ExANS – Lossless KV cache compression at 622 GB/s on H100 Hi HN,<p>We are the developers of OpenLake, an open source storage engine for KV cache offloading to remote disk and memory.<p>Once we offloaded to local disk, we realized the bottleneck is the PCIe or NIC bandwidth. We wondered whether on GPU lossless compression is viable for fast reads and lower TTFT.<p>BF16 is usually very hard to compress, (high entropy of sign&#x2F;mantissa). What surprised us is that real world KV blocks are very different. The exponent byte has a very low entropy and barely populated. Instead of compressing the whole tensor, we compress only the exponent stream on the GPU.<p>We see the following results: (H100, production KV snapshot):<p>- 1.51× lossless compression - 622 GB&#x2F;s median GPU decode<p>Decompression is ~10× faster than a 400 Gb&#x2F;s NIC bandwidth delivering data losslessly without quality change.<p>We&#x27;ve are open sourcing this as: ExANS which will be available through our vLLM and SGLang connectors on OpenLake v0.8 version. No changes are required in the inference engine.<p>I&#x27;m curious how others are handling KV transfer today. Are you using KV compression or is bandwidth not a bottleneck yet?<p>Thanks!<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;openlake-project&#x2F;openlake" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openlake-project&#x2F;openlake</a> Technical Blog: <a href="https:&#x2F;&#x2F;theopenlake.com&#x2F;blog&#x2F;exans-lossless-gpu-compression-for-bf16-kv-cache" rel="nofollow">https:&#x2F;&#x2F;theopenlake.com&#x2F;blog&#x2F;exans-lossless-gpu-compression-...</a>

Launch HN: HyperProbe (YC S26) – Agents that do read-only debugging in prod Hi HN, this is Shailendra and Karan here. We are building a fast and safe way for coding agents to debug issues live in production.<p>When prod breaks, it lets Cursor, Claude, and others drop virtual breakpoints or probes safely in your running code, and extract the exact variable values that logs don’t have.<p>All this saves time and effort for engineers who’d otherwise dig through logs and traces or redeploy with console.logs or print statements until they find the root cause.<p>Here is the link to the video that explains this: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=ivV7I--ta5c" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=ivV7I--ta5c</a><p>Agents write most of our code now. This shrinks the useful context engineers need to debug AI written code, a problem not helped by the limited telemetry added in the same code by AI.<p>So when something breaks in prod, the first instinct for an engineer is to open logs or throw them to your agents. But if the line you are looking for is not there, agents will start guessing the root cause on non-existent data, forcing you to add a log, and redeploy.<p>This analysis-inference loop of agents with existing data does not come cheap, burning a lot of tokens. And the add log, redeploy cycle is so slow and painful that it makes engineers hate on-call.<p>Our approach lets agents capture telemetry on-demand at the exact moment and point of failure, killing the log-redeploy cycle and getting the most accurate RCA while burning fewer tokens.<p>The obvious problem is making it work on a running service. You can&#x27;t pause a live service the way you&#x27;d pause a debugger on your laptop. Getting the value out of a running process safely, without pausing a thread or slowing the host is the challenge.We are making this happen.<p>Before this I ran engineering at a 100 member team. Then Karan and I spent three years on HyperTest which was a testing tool.<p>At HyperTest, we turned production traffic into integration tests using OpenTelemetry. That was production instrumentation too. The hard parts of pulling real runtime state out of a running service without breaking it, were the hard parts we learnt to put together.<p>We learnt some other lessons the hard way too. HyperTest tried to prevent bugs with better tests, and adoption was a fight every time. Calls kept getting cancelled because teams were firefighting production. Testing was hygiene. Broken prod was hair on fire. This made us see where priorities lie.<p>This seeded the idea of building a truly autonomous on-call agent i.e. one that takes an alert, probes, diagnoses and fixes it in a few minutes. But this is how it works as of now:<p>You talk to your coding agent the way you already do. Tell it what&#x27;s wrong: &quot;checkout returns 200 but some users are seeing their order fail, find out why.&quot; It locates the line in your local code, connects to us over MCP, and drops a probe on that line in the running service. The probe is read-only and sits dormant until real traffic hits. When hit, it captures the local variables at every frame of the call stack at that exact moment. It hands them to the agent, which diagnoses with real data.<p>There are two pieces. An SDK that runs inside your service, and an MCP server your coding agent talks to. The SDK is what makes setting probes (virtual breakpoints, log or metric) possible without a redeploy. In Node and Python it hooks in-process. In Java it attaches as a JVM agent, instrumenting at the bytecode level. Either way the service keeps running and serving traffic. Nothing pauses.<p>When your agent wants to look at a line, it calls the MCP server, which tells the SDK to place a probe there. When a request hits the line, the SDK captures what the probe asked for, sanitizes it in-process, and streams it back to the agent via the MCP.<p>This can run inside prod, so a probe can read any value sitting in that variable. We ensure redaction happens in-process, inside your own container&#x27;s memory. This is before anything goes on the wire. Keys like password, token, authorization, ssn and credit card are redacted by default and you add your own.<p>Also the probes read but never write, and if you want no captured state to ever leave your network you can self host the server, broker, and even the database in your infra.<p>On overhead: when idle, the SDK adds negligible memory and effectively nothing to throughput and response time. Probes only cost anything while actively capturing. Also captures are bounded. A separate monitor watches in real time and pulls every active probe if overhead ever spikes.<p>Every log-and-trace tool hands the agent data that already exists and asks it to reason backward to what probably happened. We think it is more useful to give agents eyes and ears into the running code, so they capture what they need when they need it, right at the point of failure.<p>This seems like the simplest and fastest way to debug prod incidents.<p>We’d love the community to try this in any environment to debug any known or unknown issue by just chatting with your coding agent. And let us know what more features you need to make this a truly autonomous on-call agent<p>Supported platforms: NodeJs, Java, Python.

I’m leaving OpenAI to build telepathy

Show HN: Diffing Binary Files My partner reviews a lot of P&amp;IDs (piping and instrumentation diagrams) and the adjacent files involved (excel, docx, pdfs, acd&#x2F;l5x, etc). In his company, these are usually done in Bluebeam.<p>It&#x27;s really hard to see the diff + keep track of all the revisions resulted by these iterations. They end up storing files like &quot;rev3_final_redlined.pdf&quot;. We&#x27;ve been looking for something close to Github to do these reviews, but haven&#x27;t found one easy enough for folks with no CLI experience to understand and use (happy to check out more tools if you know any).<p>So I built withkord.com to help with revisioning and diffing binary files. Any feedback is welcome.

Show HN: HUD, an open-source minimal terminal UI for ClaudeCode, Codex, OpenCode

TSON – A JSON superset with immutable, hash-pinned schemas

Building an Advanced Agentic Harness

Show HN: An AI agent that trades inside limits you set, starting on paper

Lightweight loop engineering state kernel for long-running AI agent teams. Agent-loop agnostic across Codex, Claude Code, and other coding agents, with durable goals, quota-aware auto-wake, executable todos, evidence logs, and verifiable handoffs.

Civilian plane crash in New Mexico tied to military GPS blocking

libexpat now funded by the City of Munich for up to 6 months