Show HN: LiminalML – Study ML or SWE at interview depth, grounded in your resume https://ift.tt/89gEzue

Show HN: LiminalML – Study ML or SWE at interview depth, grounded in your resume https://liminalml.com August 6, 2026 at 12:39AM

Show HN: Twocal – Calendar sync that verifies both sides actually agree https://ift.tt/GDpjIYx

Show HN: Twocal – Calendar sync that verifies both sides actually agree https://twocal.app/ August 5, 2026 at 09:37PM

Show HN: Adapt, Automatically Turns Files into REST APIs, Web UI, and MCP https://ift.tt/H8Dlr5L

Show HN: Adapt, Automatically Turns Files into REST APIs, Web UI, and MCP https://ift.tt/nJcM8H3 August 4, 2026 at 10:49PM

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