Show HN: Autobrowse – a self-improving harness for learning browser tasks https://bit.ly/4mKWqIU

Show HN: Autobrowse – a self-improving harness for learning browser tasks https://twitter.com/shreypandya/status/2047100550446280792 April 23, 2026 at 01:25AM

Show HN: Ghost Pepper Meet local meeting transcription and diarization https://bit.ly/491sT8c

Show HN: Ghost Pepper Meet local meeting transcription and diarization 100% local & private transcription engine for macOS. Captures & does speaker diarization. Originally was building as its own app, but can leverage same local models from my original push-to-talk voice transcription product so combined them into one app. https://bit.ly/4e3Ou3w April 22, 2026 at 08:19PM

Show HN: FMQL – graph query and bulk-edit CLI for Markdown and YAML frontmatter https://bit.ly/4tuazgq

Show HN: FMQL – graph query and bulk-edit CLI for Markdown and YAML frontmatter https://bit.ly/4tsH4vr April 21, 2026 at 09:08PM

Show HN: Almanac MCP, turn Claude Code into a Deep Research agent https://bit.ly/4sU5ZqB

Show HN: Almanac MCP, turn Claude Code into a Deep Research agent I am Rohan, and I have grown really frustrated with CC's search and read tools. They use Haiku to summarise all the search results, so it is really slow and often ends up being very lossy. I built this MCP that you can install into your coding agents so they can actually access the web properly. Right now it can: - search the general web - search Reddit - read and scrape basically any webpage Install it: npx openalmanac setup The MCP is completely free to use. We have also built a central store where you can contribute things you learned while exploring. If you find something useful, you can contribute it to the encyclopedia we're building at Almanac using the same MCP. https://bit.ly/3OUjo3W April 21, 2026 at 11:12PM

Show HN: A fake small claims court for petty complaints https://bit.ly/4sWCVio

Show HN: A fake small claims court for petty complaints https://bit.ly/4sRqKmT April 21, 2026 at 05:04AM

Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness https://bit.ly/3OG1lhI

Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution. Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations. A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements. This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to. An article with more technical detail: https://bit.ly/4ttPUcg https://bit.ly/48NXCph April 20, 2026 at 04:07PM

Show HN: Palmier – bridge your AI agents and your phone https://bit.ly/4d0ATb5

Show HN: Palmier – bridge your AI agents and your phone Hi HN — I built Palmier. Palmier bridges your AI agents and your phone. It does two things: 1. It lets you use your phone to directly control AI agents running on your computer, from anywhere. 2. It gives your AI agents access to your phone, wherever you are — including things like push notifications, SMS, calendar, contacts, sending email, creating calendar events, location, and more. A few details: * Supports 15+ agent CLIs * Supports Linux, Windows, and macOS * What runs on your computer and your phone is fully open source * Works out of the box — no need to set up GCP or API keys just to let agents use phone capabilities * Your phone can act as an agent remote: start tasks, check progress, review results, and respond to requests while away from your desk * Your phone can also act as an agent tool: agents can reach into phone capabilities directly when needed * Optional MCP server: if you want, Palmier exposes an MCP endpoint so your agent can access phone capabilities as native MCP tools. This is optional — you can also use Palmier directly from the phone app/PWA, with those capabilities already built in * Still in alpha stage, with bugs. Opinions and bug reports very welcome The basic idea is that AI agents become much more useful if they can both: * interact with the device you actually carry around all day * be controlled when you are away from your computer Palmier is my attempt at that bridge. It already works with agent CLIs like Claude Code, Gemini CLI, Codex CLI, Cursor CLI, OpenClaw, and others. You can run tasks on demand, on a schedule, or in response to events. Would especially love feedback on: * whether this feels genuinely useful * which phone capabilities are most valuable * which agent CLIs I should support next * what feels broken, awkward, or confusing Site: https://bit.ly/42omNLm Github: * https://bit.ly/48TuQn5 * https://bit.ly/3Qd8CGx Happy to answer questions. https://bit.ly/48TuQn5 April 21, 2026 at 03:31AM

Show HN: Mimi in the browser – hear the semantic/acoustic split https://bit.ly/4sJTH3O

Show HN: Mimi in the browser – hear the semantic/acoustic split https://bit.ly/4tvRric April 21, 2026 at 12:33AM

Show HN: Brygga – A modern, fast, feature-rich IRC client for macOS https://bit.ly/4cTOrpF

Show HN: Brygga – A modern, fast, feature-rich IRC client for macOS Brygga is in early development. The core client works end-to-end (connect, join, send, receive, persist) but many features you'd expect from a mature IRC client are still missing. Repo: https://bit.ly/4mBr8UU April 20, 2026 at 12:11AM

Show HN: TRELLIS.2 image-to-3D running on Mac Silicon – no Nvidia GPU needed https://bit.ly/48LEND9

Show HN: TRELLIS.2 image-to-3D running on Mac Silicon – no Nvidia GPU needed I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as H100 (where it takes seconds), but it works offline with no cloud dependency. https://bit.ly/4cB0fvE https://bit.ly/4cB0fvE April 20, 2026 at 01:07AM

Show HN: How context engineering works, a runnable reference https://bit.ly/4sU6lxC

Show HN: How context engineering works, a runnable reference I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or vbrownbags. Hopefully it's useful. https://bit.ly/3OSFP9H April 17, 2026 at 07:20PM

Show HN: Coelanox – auditable inference runtime in Rust (BERT runs today) https://bit.ly/3OMabe0

Show HN: Coelanox – auditable inference runtime in Rust (BERT runs today) PyTorch and ONNX Runtime tell you what came out. They can't tell you what actually ran to get there — which ops executed, in what order, on what inputs. A model gets packaged into a sealed .cnox container. SHA-256 is verified before a single op executes. Inference walks a fixed plan over a minimal opset. Every run can emit a per-op audit log: op type, output tensor hash, output sample — cryptographically linked to the exact container and input that produced it. If something goes wrong in production, you have a trail. Scalar backend today — reference implementation and permanent fallback when hardware acceleration isn't available. Audit and verification is identical across all backends. SIMD next, GPU after that. Input below is synthetic (all-ones) — pipeline is identical with real inputs. github.com/Coelanox/CLF Audit example: { "schema": 2, "run": { "run_id": "59144ede-5a27-4dff-bc25-94abade5b215", "started_at_unix_ms": 1776535116721, "container_path": "/home/shark/cnox/models/output/bert_base_uncased.cnox", "container_sha256_hex": "184c291595536e3ef69b9a6a324ad5ee4d0cef21cc95188e4cfdedb7f1f82740", "backend": "scalar" }, "input": { "len": 98304, "sha256_hex": "54ac99d2a36ac55b4619119ee26c36ec2868552933d27d519e0f9fd128b7319f", "sample_head": [ 1.0, 1.0, 1.0, 1.0 ] }, "ops": [ { "op_index": 0, "op_type": "Add", "out_len": 98304, "out_sample_head": [ 0.12242669, -4.970478, 2.8673656, 5.450008 ], "out_sha256_hex": "19f8aa0a618e5513aed4603a7aae2a333c3287368050e76d4aca0f83fb220e78" }, { "op_index": 1, "op_type": "Add", "out_len": 98304, "out_sample_head": [ 0.9650015, 0.23414998, 1.539839, 0.30231553 ], "out_sha256_hex": "7ae2f025c8acf67b8232e694dd43caf3b479eb078366787e4fdc16d651450ad4" }, { "op_index": 2, "op_type": "MatMul", "out_len": 98304, "out_sample_head": [ 1.0307425, 0.19207191, 1.5278282, 0.3000223 ], "out_sha256_hex": "44c28e64441987b8f0516d77f45ad892750b3e5b3916770d3baa5f2289e41bdd" }, { "op_index": 3, "op_type": "Gelu", "out_len": 393216, "out_sample_head": [ 0.68828076, -0.0033473556, 1.591219, -0.16837223 ], "audit_elided": "hash_skipped: len 393216 > max 262144" } https://bit.ly/4mEV1DY April 18, 2026 at 09:37PM

Show HN: Sostactic – polynomial inequalities using sums-of-squares in Lean https://bit.ly/4vAzfFm

Show HN: Sostactic – polynomial inequalities using sums-of-squares in Lean Current support for nonlinear inequalities in Lean is quite limited. This package attempts to solve this. It contains a collection of Lean4 tactics for proving polynomial inequalities via sum-of-squares (SOS) decompositions, powered by a Python backend. You can use it via Python or Lean. These tactics are significantly more powerful than `nlinarith` and `positivity` -- i.e., they can prove inequalities they cannot. In theory, they can be used to prove any of the following types of statements - prove that a polynomial is nonnegative globally - prove that a polynomial is nonnegative over a semialgebraic set (i.e., defined by a set of polynomial inequalities) - prove that a semialgebraic set is empty, i.e., that a system of polynomial inequalities is infeasible The underlying theory is based on the following observation: if a polynomial can be written as a sum of squares of other polynomials, then it is nonnegative everywhere. Theorems proving the existence of such decompositions were one of the landmark achievements of real algebraic geometry in the 20th century, and its connection to semidefinite programming in the 21st century made it a practical computational tool, and is what this software does in the background. https://bit.ly/4cSeiOP April 18, 2026 at 11:36PM

Show HN: Mind-OS – First free online AI dependency self‑assessment https://bit.ly/3Qh7L7A

Show HN: Mind-OS – First free online AI dependency self‑assessment https://bit.ly/4epeJkU April 17, 2026 at 10:40PM

Show HN: Ask your AI to start a business for you, resolved.sh https://bit.ly/4mAJc1z

Show HN: Ask your AI to start a business for you, resolved.sh Start with a FREE instant website for your AI on the open internet, then work with it to build a business that sells specialized datasets, files, premium reports, blogs, courses and more. https://bit.ly/4mx3h8Q April 17, 2026 at 04:31AM

Show HN: Free API and widget to look up US representatives https://bit.ly/4ciVtEs

Show HN: Free API and widget to look up US representatives https://bit.ly/4mAHLQt April 17, 2026 at 01:45AM

Show HN: Spice simulation → oscilloscope → verification with Claude Code https://bit.ly/488OVFT

Show HN: Spice simulation → oscilloscope → verification with Claude Code I built MCP servers for my oscilloscope and SPICE simulator so Claude Code can close the loop between simulation and real hardware. https://bit.ly/4cuNvqx April 17, 2026 at 01:37AM

Show HN: I built a Wikipedia based AI deduction game https://bit.ly/4vtN4pb

Show HN: I built a Wikipedia based AI deduction game I haven't seen anything like this so I decided to build it in a weekend. How it works: You see a bunch of things pulled from Wikipedia displayed on cards. You ask yes or no questions to figure out which card is the secret article. The AI model has access to the image and wiki text and it's own knowledge to answer your question. Happy to have my credits burned for the day but I'll probably have to make this paid at some point so enjoy. I found it's not easy to get cheap+fast+good responses but the tech is getting there. Most of the prompts are running through Groq infra or hitting a cache keyed by a normalization of the prompt. https://bit.ly/4muibN6 April 16, 2026 at 01:13AM

Show HN: StockFit API – structured SEC EDGAR data with a free tier https://bit.ly/3O7Ljx7

Show HN: StockFit API – structured SEC EDGAR data with a free tier https://bit.ly/4ct3e9A April 15, 2026 at 02:53AM

Show HN: Keynot – Kill PowerPoint with HTML https://bit.ly/4cm4on7

Show HN: Keynot – Kill PowerPoint with HTML https://bit.ly/4tPn0Db April 15, 2026 at 03:05AM