Show HN: Product analytics (and evals) for agent sessions on your MCP https://ift.tt/jfJ2Ih7

Show HN: Product analytics (and evals) for agent sessions on your MCP Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought. You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix them. Here is a quick demo: https://youtu.be/ZFlvquhyNMQ The story behind this is that we initially launched Armature as a standalone testing tool ( https://ift.tt/jdGrQ7t... ) that could naturally be used through an MCP itself. We quickly realized we had no idea how our users were using Armature MCP and if they were satisfied with it or frustrated. It’s something we had also experienced in our previous companies: Louis built MCPs exposed to millions of users and Theo was a Forward Deployed Engineer at Palantir before joining a Datadog spin-off as Founding Engineer. Both testing and product analytics had always been real pains when exposing a product to agents but we always thought there wasn’t much we could do about analytics because the conversation lived in our users’ AI client. Then it struck us: what if we asked the agents why they were making this or that tool call? And what’s the user's intent or potential frustration? So we started experimenting with MCP instrumentation and the use-cases actually surprised us! Many of our first customers had implemented workarounds for their CI to trigger new tests or for their coding agents to fetch the results efficiently. Even though we talked to our first users regularly, they had never shared this feedback with us. We then built automations to automatically cluster use-cases, identify issues frequently encountered and let our own coding agents fix them. When our CTO friends heard about this, they wanted to try it for themselves so we gave them access to a cloned version of our internal product and they started sharing feedback like they never did on our “real” product! That’s when we decided to start working seriously on MCP Analytics as a product. At first we were afraid of degrading MCP performance so we iterated until we reached the exact same success rate as without our instrumentation (89.17 % vs 89.15 % pass rate out of 870 runs). Then privacy was an obvious constraint so we applied the same methods we had learned from working with banking data or building sensitive data scanning in logs. Today, redaction runs client-side before reaching our servers. There are still a lot of things we haven’t fully figured out: not all fields are equally filled by all models, session fingerprinting for serverless / stateless MCPs isn’t perfect, and use-case clustering remains to be optimized. But we are finally launching our analytics product to everyone, self-serve at https://armature.tech with a set-up that takes less than 5 minutes and a generous free tier. And now we are working on fully closing the loop, bringing evals back in our product so we can: identify top workflows and issues -> recommend fixes and improvements -> test fixes at scale on the same workflows run by users, across all harnesses and models -> open PRs to ship fixes directly. The evals can be generated automatically from the session analytics so you can catch every regression and can test every improvement’s real impact across all models and harnesses before shipping it. Here’s an example to make it more concrete: 10 days ago, a marketing automation platform which has had early access to what we built for weeks identified thanks to MCP Analytics that users were frustrated not being able to change their target audience after campaign creation. So they shipped the feature and tested it successfully locally with Claude Code on Fable 5. Then a few days later when preparing their new MCP public release, they ran a suite of evals on Armature and realized that small models could hallucinate audience_ids which would lead their MCP to send the campaign to ALL their contacts by default (which could obviously lead to disasters in prod). This is the kind of story that makes what we are building feel so helpful! Now, the most useful feedback for us would be to know what’s still missing in our product so you can feel you are now in full control of the “Agent Experience”. And if you run an MCP in production we’d also love to know: what do you do today to know if agents succeed and if the users behind them are happy? https://armature.tech/ August 3, 2026 at 11:17PM

Show HN: Schmess – chess with no turns; pieces freeze on cooldown after moving https://ift.tt/SGOtyIz

Show HN: Schmess – chess with no turns; pieces freeze on cooldown after moving Solo dev here. Schmess is chess with the turn structure removed. Both players move whenever they have a free slot, and every piece freezes on a cooldown after it moves. It's on a half board, with 100+ puzzle stages. If two people played this over a physical board they'd scrape each other's fingers raw - that's roughly the tempo. Plays in the browser, no signup. 29 stages free, one-time unlock for the rest ($3.99 / Rs 149). No subscription, no ads. It's the second of a few small-board variants I'm building (halfchess was the first, more coming). Building a chess engine for a game with no turns turned out to be the hard part - I've put the war stories in a comment below. https://schmess.com/ August 2, 2026 at 11:47PM

Show HN: TamedTable, AI ETL in Natural Language https://ift.tt/S1NzTmt

Show HN: TamedTable, AI ETL in Natural Language Hi HN, TamedTable is an LLM harness for data ETL. And yes, it was developed using AI, meaning you can take the entire specification and recreate it to your desires: https://ift.tt/NpDiuC7 https://ift.tt/cR2gqbT August 2, 2026 at 02:51PM

Show HN: SteerPlane – Deterministic runtime guardrails for AI agents https://ift.tt/avByo0p

Show HN: SteerPlane – Deterministic runtime guardrails for AI agents https://ift.tt/AGPoslX August 2, 2026 at 01:09AM

Show HN: A second pair of eyes for electrical schematic review https://ift.tt/NBoDW0r

Show HN: A second pair of eyes for electrical schematic review https://bvcircuits.com/ July 31, 2026 at 10:16PM

Show HN: Tally – check a spreadsheet's numbers against their source, in-browser https://ift.tt/zjo9LCd

Show HN: Tally – check a spreadsheet's numbers against their source, in-browser https://ift.tt/6oPxIRZ July 31, 2026 at 12:55AM

Show HN: I made a game where you build a CPU from logic gates https://ift.tt/yCL26kh

Show HN: I made a game where you build a CPU from logic gates I built ChipBuilder to make computer architecture more approachable through interactive puzzles. You start with basic logic gates like AND, OR, and NOT, then gradually combine them into adders, multiplexers, memory, an ALU, and eventually a complete CPU. Once you've built the hardware, you can write assembly programs that run on the processor you created. The game is inspired by courses like Nand2Tetris, but everything runs directly in the browser with a visual circuit editor, simulations, and progressively harder challenges. https://ift.tt/SvW4Ycu July 30, 2026 at 06:33PM

Show HN: Rivora – An open-source memory layer for engineering tools https://ift.tt/S0DfdEG

Show HN: Rivora – An open-source memory layer for engineering tools https://ift.tt/3W2deTh July 29, 2026 at 11:04PM

Show HN: NoClick – Build always-on agents with your existing AI subscriptions https://ift.tt/5ugHQiN

Show HN: NoClick – Build always-on agents with your existing AI subscriptions Hey HN! Been working on a platform that lets you build always-on background AI agents that work with your existing subscriptions/harnesses of choice (Claude Code, Codex, OpenCode, Hermes, OpenClaw). Works in three steps: - Choose your agent harness (CC/Codex/OpenCode/Hermes/OpenClaw) and connect your corresponding subscription - Select the tools across 150+ integrations you want the agent to have access to - Decide when the agent should run (recurring schedule, app event, webhook, etc) All the infra behind the scenes (spinning up a sandbox to run the harness, connecting selected tools to it, persisting files across agent cold starts, observability for tool calls, etc) is automatically handled. Happy to answer any questions! https://ift.tt/Q9Nxskd July 28, 2026 at 11:48PM

Show HN: Open-source, Long-horizon cite-able memory for multi-agent systems https://ift.tt/3adKFUk

Show HN: Open-source, Long-horizon cite-able memory for multi-agent systems Two agents that share no model, no vendor, and no trust can cite the same signed fact and each verify it alone. Devices e.g. satellites, robots, CCTVs fill the memory today; any machine that watches the world joins by proving how it ran. https://ift.tt/ich7g5p July 28, 2026 at 10:47PM

Show HN: Let's Seal – Let's Encrypt for document signing, free and self-hosted https://ift.tt/GaPiSQD

Show HN: Let's Seal – Let's Encrypt for document signing, free and self-hosted TLDR, Let's Seal gives the finger to Adobe and every doc signing tool (docusign, google, etc) who pay to play with the Adobe Approved Trust List and then charge you for something that should be free. Currently even the person checking if a document/contract is sealed or code is authentic has to also be inside the same Adobe walled garden too. Verification, the part that should be free is the part everyone charges for. Thats the shape Let's Encrypt fixed for TLS, and I wanted the same thing for documents and files. The core idea therefore needed to go a bit beyond e signatures and i created an open standard (SEAL), plus free tools that implement it. When you seal a file, three independent things happen. 1. it gets a signature from a certificate authority, chaining to a public root. 2. its record is appended to an RFC 6962 transparency log. and 3. its SHA256 is timestamped on a public blockchain (Bitcoin) via OpenTimestamps. Those three give you integrity, transparency and a timestamped proof. And importantly, none of those depend on Let's Seal and none are gated. You can verify with the tools you already have, no Let's Seal account and no Let's Seal software. A sealed PDF carries a standard PAdES signature, so any PDF reader validates it. A sealed build artefact carries a cosign compatible signature and a SLSA provenance attestation. The Bitcoin timestamp verifies with stock ots. 3 ways to use it. 1. The free web app. We kindly have backing from Backblaze to cover storage costs for the foreseeable. So you can upload or issue any number of documents, get a public proof page at /d/ and verify it at https://ift.tt/45eYC9k for free. Multiple accounts, multiple seats, enterprise functions. Free. 2. Self host the whole thing. Apache-2.0, one Next.js app plus a signing service that holds the CA key on localhost. Storage is any S3-compatible bucket or local disk. If you'd rather run your own root of trust, you can. 3. Programmatically. via the CLI and a hosted API. This is the Let's Encrypt/certbot angle. Seal or anchor things from CI, or have a backend seal every invoice or report as its generated. The CLI is sealbot. It runs anywhere Node runs (npx sealbot) and there are native binaries for macOS, Linux and Windows with no runtime needed. Theres a GitHub Action wrapping the same tool, so a release workflow can seal its own artifacts. Its what proves our own releases. KYC is semi-handled (to a degree) it's hard to do for free (at least for now), but issuers (your companies or websites) domains can be authenticated with a DNS record added, which proves the issuer has control over a domain. Sign-in can be authenticated to an email via Google Sign in and a few others will be added to the web app in time (Same as Docusign currently). Ideas welcome on future KYC should there be a demand. Feedback welcome on the standard (SPEC.md in the repo). Repo: https://ift.tt/dMuwXT5 Site: letsseal.org Thx https://ift.tt/dMuwXT5 July 27, 2026 at 10:52PM

Show HN: FeyNoBg – Automatic background removal model and training library https://ift.tt/42yG8wW

Show HN: FeyNoBg – Automatic background removal model and training library Hey HN, I’m Shreyash from Feyn. We help companies build custom models from their data. Today, we’re releasing FeyNoBg, an automatic background removal model. Alongside it, we're open-sourcing NoBg, the Python library we built to train and run it. Try the model here: https://ift.tt/5sVGQPN . Check out the library here: https://ift.tt/pdbw2WT Some sample outputs: (1) Soccer Freekick: https://drive.google.com/file/d/1MZkAGLwbhNVOZ0Oi7XvpCfSEu9Q... (2) Hair in wind: https://drive.google.com/file/d/1Odc2m0XMVH9uZtvI_KjaRbXzhLL... (3) Bicycle with visible spokes: https://drive.google.com/file/d/1h99ahjfrtS1MFQJJgiKE2fuM3HZ... (4) Live Demo video: https://youtu.be/b1heHPvY8BM Background removal separates an image's subject from its surrounding. We've all tried it at some point. Often it is to reuse the subject in a different artifact. Nowadays, it is common to make chat stickers out of it. It is one of the most common but under-appreciated uses of AI. It is also surprisingly complex. Models can be easily confused by camouflage, motion blur, or fine structures like hair. The task requires two skills. First, a model has to identify the foreground. Second, it has to trace the foreground’s boundary and estimate an opacity value for each pixel. Generally, these skills are taught with different datasets. That creates a failure point. A poor training mix can improve one skill at the expense of the other. We saw this in our controlled evaluation. A training run with just the MaskFactory dataset improved on the CAMO benchmark but regressed on DIS5K. For FeyNoBg, we took an interpretability-first approach to training. We first studied how BiRefNet’s stages contribute to finding the foreground and reconstructing its boundary. We discovered that the third stage of it's feature extractor holds a lot of information. Both localization and boundary reconstruction depend heavily on the feature map produced here. This led us to expand this stage from 18 to 24 blocks while preserving the pre-trained weights. We then trained FeyNoBg on 26.1K diverse examples assembled from 10 datasets. The goal was to improve foreground identification and boundary precision without sacrificing either one. Across eight benchmarks, FeyNoBg achieves the best published score on four and comes within 2% of the leader on the rest. Building FeyNoBg also exposed a tooling problem. Image matting models are usually released as isolated repositories with incompatible preprocessing, training, and evaluation code. We built NoBg to solve this. NoBg puts these workflows behind one Python interface. It supports BiRefNet today, with more architectures coming. We hope you build something exciting with it! Happy to answer any questions! https://ift.tt/T3OSXuj July 27, 2026 at 11:59PM

Show HN: Infrawrench – a tool to manage cloud and svcs with workflows and chat https://ift.tt/DAVTva5

Show HN: Infrawrench – a tool to manage cloud and svcs with workflows and chat https://infrawrench.com July 27, 2026 at 11:25PM