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Job seekers giving up: Labor force participation falls to lowest in 50 years

Show HN: ctx – Search the coding agent history already on your machine Coding agents don&#x27;t have long-term memory.<p>But you do have months of full-fidelity agent transcripts stored on your machine.<p>A simple solution that goes a long way: ingest those transcripts and logs into a structured SQLite database, then search them with ranked text match. Everything is fully local and doesn&#x27;t require anything fancy like a graph database or hosted memory service.<p>This is the idea behind ctx, a Rust CLI that handles the ingestion and searching.<p>We give our agents a skill that tells them to reference past sessions before working in an area. Usually we do this through an &quot;Agent History Research Subagent&quot; whose job is just to prepare a short brief covering any relevant history before the task begins.<p>A real example: sometimes our test suite runs would fail because disk was full on the runner. The correct approach was to run the cleanup runbook, but the root cause of the failure was not clear to the agents, so they would think it was a test regression and go down the wrong rabbit hole debugging. When the agent searched history, it realized this failure had been encountered before and found the right workaround immediately. That got the agent onto the right cleanup path, and later we improved the log output so the same failure would be clearer next time. It&#x27;s a boring story, but it&#x27;s real agent productivity.<p>Another nice use case is quickly generating session transcripts for sharing. You can exclude the noisy intermediate messages, so the transcript shows the important parts of the session more cleanly. Try attaching a session transcript to your next PR so your teammate and their agent can review the provenance and prompting behind the change.<p>If you&#x27;re up for an additional challenge, ask your agent to &quot;exhaustively review all agent history in this repo and find where the SDLC is struggling or isn&#x27;t agent-native&quot;. Using past sessions to recursively improve the agentic SDLC is a loop that we&#x27;re using a lot today.<p>If you try it out, please let us know what you think!

Show HN: ZkGolf Zero-Knowledge Proofs (ZKPs) let an untrusted proved show that computation was executed correctly without revealing the inputs to the verifier. However to prove anything, the computation first has to be expressed as a circuit: a system of polynomial equations (constraints) over a finite field. Circuits are the assembly language of zk and every constraint costs prover (and sometimes verifier) time, so production circuits are aggressively hand-optimized.<p>Over the last months, we have been experimenting with writing formal specifications instead and letting LLMs produce the circuits: as long as they could prove that their implementation was correct. It started with SHA-256: we hand wrote a specification in Lean for SHA-256 compression, and then we asked LLMs to write the circuit, targeting R1CS arithmetization and large fields.<p>It took a few hours of work for Opus 4.7, and some light steering into the right direction, but in the end the model came up with a reasonable implementation. We then asked the LLM to aggressively optimize the circuits, by driving down a cost metric of the circuit (number of constraints). We immediately got very promising results, just by asking to come up with optimization ideas, implement them and prove that the new circuit still satisfies soundness and completeness. Sometimes, it came up with unsound optimizations, however, since it could not prove them, it backtracked and got itself back on to the right approach.<p>The result was a (non-deterministic) circuit beating the current, human optimized, state of the art for SHA256 compression. This experience lead us to create &quot;zk.golf&quot; which is an open competition to produce optimized, formally verified circuits to lower the bar for the use of ZKPs and make their application more efficient.<p>Come play (<a href="https:&#x2F;&#x2F;zk.golf&#x2F;llms.txt" rel="nofollow">https:&#x2F;&#x2F;zk.golf&#x2F;llms.txt</a>) and learn about formal verification.

Launch HN: Manufact (YC S25) – MCP Cloud Hi HN, we are Pietro and Luigi, cofounders of Manufact (<a href="https:&#x2F;&#x2F;manufact.com">https:&#x2F;&#x2F;manufact.com</a>), a cloud for MCP apps and servers. We used to be called mcp-use, and still build open source SDKs for MCP under that name: <a href="https:&#x2F;&#x2F;github.com&#x2F;mcp-use&#x2F;mcp-use" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mcp-use&#x2F;mcp-use</a>. We did a Show HN about that last year: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44747229">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=44747229</a>.<p>Today we want to tell you about our cloud product, Manufact, which is to mcp-use as Vercel is to Next.js. Manufact is an MCP vertical cloud designed for dev teams putting MCP Apps and servers in production.You can ship, iterate on, test and monitor your MCPs, and get them ready for the store submissions. All with the best developer and agent experience in mind.<p>Here is a demo video of the product: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=R2rbr5OT9LI" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=R2rbr5OT9LI</a>.<p>We have been working on MCP since April 2025. Our first focus was making it easy to build agents that could use any MCP server, and a lot of people started using our SDKs. Then the harness revolution kicked off: Claude Code, Claude Cowork, ChatGPT, Codex, OpenCode started shipping agent harnesses that made most standalone agent frameworks redundant. That pushed us to the other side of the connection, the servers. If agents were going to consolidate into a few harnesses, then first-class integration with the rest of a company&#x27;s systems (i.e. MCP) would become the thing that mattered, so we started building up our server SDKs.<p>Then in succession:<p>1. Oct 2025. ChatGPT Apps SDK. OpenAI brings app UIs to ChatGPT, built on top of MCP and the work of mcp-ui. 2. Late 2025. The stores open. ChatGPT starts accepting app submissions, Claude grows its connector directory with selected partners. 3. Jan 2026. MCP Apps becomes official. SEP-1865 merges as the first MCP extension (io.modelcontextprotocol&#x2F;ui): one UI standard any host can render.<p>Today, all the major clients fully support MCP and are opening marketplaces of reviewed MCPs that can be one click installed. All major tech companies have an MCP server, and many of those are reporting that already 15+% of their usage comes from their MCP, and we start to have a good way to distribute them just now.<p>MCP can return fully interactive UIs. So companies can (1) display data in more meaningful ways to their users (e.g. analytics, ecommerce) and (2) display their branding in some of the most used products on the planet (ChatGPT, Claude etc). Numbers: an engineer at Amplitude reported that their MCP saw a 2x increase in retention after adding UI to their MCP.<p>Clients (Claude, ChatGPT, Cursor) are starting to dynamically present MCP servers&#x2F;apps to users, based on their intent. Products will be organically discovered on the chats!<p>We feel that MCP is reaching its maturity moment. Now that MCPs are starting to be easy to install and discover, there is going to be a huge incentive for users to use them and for companies to create them:<p>1 - Most work is already done from AI chats, this is not going to stop, MCP gives you a way to interact with products without manually using their dashboards.<p>2 - MCP allows you to bring the context together in one place: you can read an email, create a ticket while plugged into the source code of your product, or your knowledge base. Aggregation of products that was not possible before, will happen in the chat, orchestrated by increasingly intelligent models.<p>If AI apps (Codex, Claude Desktop) are the new browsers, as PG said in a recent tweet <a href="https:&#x2F;&#x2F;x.com&#x2F;paulg&#x2F;status&#x2F;2069080429236191504" rel="nofollow">https:&#x2F;&#x2F;x.com&#x2F;paulg&#x2F;status&#x2F;2069080429236191504</a>, then MCPs are the new websites.<p>But there is a catch:<p>- Submission process on the stores is still quite tricky, manual and takes up valuable time. - Hardly anybody knows how to design a good MCP: most of them are 1:1 proxies of the API and are abandoned, since being one shotted a few months ago. - The MCP Spec advances quickly and it is not easy to keep track of the changes, and what they mean for your server. - Auth is still a mystery for most teams (API key in the URL ???). - Most companies are not even aware that MCPs can return interactive UIs. - Clients still have to consolidate behavior, some do dynamic tool discovery, some don&#x27;t, some persist authentication properly some don&#x27;t.<p>We built Manufact and mcp-use to solve these problems. Our SDKs help them build good MCPs, our inspector helps them test locally, and our cloud helps them ship&#x2F;publish and monitor them in production.<p>To deploy on Manufact you just need to connect a Github app, pick the repo, we&#x27;ll detect the framework you are working with and get you a live MCP url as soon as possible.<p>In our platform, that live URL will be used to give you a chat where you can try&#x2F;debug your MCP immediately and share it with your team. If you push an update on a new experimental branch, you&#x27;ll be able to test that as well thanks to preview deployments.<p>Once your server is ready to go live, we help you make sure that it does not break. You can configure automated tests that will take your MCP server, install it in ChatGPT and Claude and test it. We do not test the model, we test the client (model + harness). This way you reliably know if your server breaks where people use it.<p>Since publishing on the store is a major distribution unlock for companies (your MCP can be dynamically discovered and one click installed across Claude products, and ChatGPT), we collected a set of requirements that will keep your submission from being rejected. You check this locally before going through the actual review process.<p>Once your server is live, you&#x27;ll want to understand how it is used. Our analytics are designed for MCP, so you&#x27;ll know how many users are hitting your MCP, how many tool calls you receive, from which client.<p>You can try out <a href="https:&#x2F;&#x2F;manufact.com">https:&#x2F;&#x2F;manufact.com</a> for free today. We have usage-based pricing and on our free account we give free credits for you to try it out. If you have an MCP already, just connect your Github repo and deploy, if not you can build one using our skill and SDKs pretty simply (we will guide you in the onboarding).<p>We would love to hear feedback about the product in the comments, and hear thoughts from everyone about MCP. Thanks! :)

Show HN: Enola-A deterministic architecture graph for developers and AI agents Together with a friend, we were developing a golf application. Our codebase grew rapidly and became split between multiple repositories: the iOS app, Android app, backend, front-end, and extra tooling. Both of us also work in larger scale-ups, and we saw the same problem: understanding large distributed codebases becomes progressively harder. Yay for microservices.<p>It takes time to understand and answer questions like: - <i>What calls this function?</i> - <i>What is the impact of changing this interface?</i> - <i>Is this code actually reachable and used?</i><p>Not a secret that both of us embrace the leverage AI coding agents bring. But … AI agents spend a surprising amount of time understanding and rediscovering architecture. For them, architecture is a result of greps and, at times, assuming dependencies. With a new session, they rediscover the architecture again. Yet, architecture is deterministic. To introduce any changes, you need to understand the architecture.<p>Over months, we optimised and built Enola to manage that hurdle.<p>Enola is an open-source architecture engine that exposes an MCP server. Index any codebase into a persistent knowledge graph. If needed, combine multiple repositories into a graph of graphs. While constructing the graph, Enola parses the repository without using an LLM. The graph is built deterministically from source code. Outcome: A structured, deterministic architectural model of your system <i>(a collection of multiple repositories)</i>.<p>Why open-source? Our goal is to provide engineering tools to manage the <i>“code inflation”.</i> There is a lot more code being produced, and codebases grow faster and faster. But the architectural integrity is still needed. Enola exists because software engineering still begins with understanding a system before changing it.<p>Key Features <i>(subset)</i>:<p>1. Impact Analysis: Determine the &quot;blast radius&quot; of a change by querying the graph of relationships between symbols, modules, and API routes. Simply ask: <i>“If I change this, what breaks?”</i><p>2. Dead Code Discovery: Identify unused code paths and orphaned components that aren&#x27;t reachable through your defined entry points.<p>3. Dependency Analysis (<i>We called it traverse, because why not)</i>: Trace the dependencies, both downstream and upstream. You can simply ask Enola: “What <i>depends on X?”</i><p>4. Multi-Repo Context: Enola supports a &quot;graph of graphs,&quot; allowing you to index and query relationships across as many repositories as your architecture requires. So stack them up!<p>5. Performance: Enola runs fast, given its architecture, naturally depending on your codebase. Give it a try! Curious.<p>We are open-source, building in public. You can find the documentation and source in the link above.<p>If you have a complex codebase and would be willing to test Enola, I’d appreciate the feedback. Tell us what works, what is missing.

Show HN: CLI tool for detecting non-exact code duplication with embedding models

No LLM Code in Dependencies

Show HN: Mail Memories – A desktop app to rescue photos from Gmail Hey HN, I’m the creator of Mail Memories. Like many of you, I&#x27;ve had my Gmail address for more than 20 years. A few years ago, I got curious and wanted to see what photos were buried deep in my account. I ended up finding lots of &quot;lost&quot; pictures of old friends, family members, and a ridiculous number of vintage memes.<p>I originally built and launched this as a SaaS, but even with code and policies in place that kept users&#x27; photos private, I figured everyone would feel more comfortable with a desktop app.<p>So, I threw out the server architecture and completely rewrote it as a 100% local desktop app for Mac and Windows.<p>How it works now: The app connects directly to Google&#x27;s server from your computer, processes everything entirely on your system, and saves photos straight to your hard drive.<p>You can download your 50 oldest photos for free (no credit card required) just to see what&#x27;s in there. If you want to download all the pictures in your account, it&#x27;s a one-time payment of $29. No subscriptions.<p>If you have an old, pre-2010 Gmail account, definitely give it a spin. You&#x27;ll be surprised at what you find deep in your archive.<p>I&#x27;d love to hear your feedback on the layout, scanning performance, or anything else.<p>TL;DR: I turned my SaaS into a local desktop app (Mac&#x2F;Windows) that recovers decades of forgotten photos from your Gmail. 100% local, no cloud, no subscriptions, no AI.

Show HN: ZeroFS – A log-structured filesystem for S3

Show HN: I built an open-source alternative to Claude Cowork Hey HN,<p>A few months ago, I tried to automate some of my work with the popular AI agent OpenClaw, and then I quickly realized how difficult it is to get it to work with APIs and third-party services securely, which is essential for a lot of work-related tasks.<p>Then I realized OpenClaw is more of a personal assistant and it was not designed to get actual work done as a coworker. So I started to build Valmis, an alternative to OpenClaw that works with more than 100 apps and services, with security being the priority.<p>Valmis addresses the security issue by designing a proxy system: dockerized agent runtime can only request the host machine to make API requests by providing the relevant credential ID. The host then makes the actual request and returns the JSON data to the agent runtime. With this design, you can even turn off the internet access of the agent container while making it work.<p>Our proxy system now supports 100+ business and productivity integrations, including all Google Workspace apps, Slack, Notion, HubSpot, Salesforce, and Figma.<p>One of the coolest features of Valmis is the automated workflow. You can automate multi-step workflows using our workflow builder. Each workflow can be triggered by cron, webhooks, app events, and it supports conditions and loops.<p>I&#x27;d be happy to answer any questions in the comment section.

This blog is written in en-GB

Clean Code concepts adapted for JavaScript

Langflow is a powerful tool for building and deploying AI-powered agents and workflows.

Use Codex from Claude Code to review code or delegate tasks.

Specification and documentation for Agent Skills

🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman

Show HN: Kube-insight – retained Kubernetes evidence for incident investigations

The primary purpose of code review is to find code that will be hard to maintain

Show HN: I measured the half-life of 41,301 Show HN launches. It's 7 hours I scraped every Show HN from the last 12 months (41,301 posts) plus the full comment tree of every launch with 10+ comments, ~100k comment timestamps, all from the Algolia HN API.<p>The median launch gets 2 points and 0 comments. For launches that do get traction, half the comments they&#x27;ll ever get arrive within 7.2 hours and 90% within 26, and the top decile decays on the same clock as everyone else.<p>Vote timestamps aren&#x27;t public, so comment timing is the attention proxy; caveats are in the post. Everything reproduces from the repo with one command (<a href="https:&#x2F;&#x2F;github.com&#x2F;jonnonz1&#x2F;hn-attention-cliff" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;jonnonz1&#x2F;hn-attention-cliff</a>), and every number in the post maps to a named function. Keen to hear where the methodology falls short

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