Show HN: A $25 DIY alternative to $159 AI voice recorders – BYOK or local https://ift.tt/kuI5R2s

Show HN: A $25 DIY alternative to $159 AI voice recorders – BYOK or local https://zephclick.com September 25, 2026 at 12:51AM

Show HN: Canary (YC) – Independent verification for AI code https://ift.tt/uaHs6Ay

Show HN: Canary (YC) – Independent verification for AI code Hey HN, we are Aakash and Viswesh and we are building Canary ( https://ift.tt/EWiU2Pw ) - independent verification for AI code. Claude/Codex calls Canary with the changesets, intended behaviour and team knowledge. Canary then deploys agent swarms to investigate potential failures and test suspected runtime bugs in remote sandboxes. To try it on your repository, paste this into your coding agent: Install the Canary CLI with npm i -g @runcanary/cli, then run canary skills and follow its instructions to onboard this repository. Verification starts with what software is supposed to do and most importantly what it must never allow. This means investigating how inputs, permissions, state, timing, dependencies etc interact with each other. Intent is not always fully declared as well but many expectations are clear: private files should stay private, credentials should not leak, and retries should not create unintended duplicate effects. We believe the future is a unified and independent verification system that starts with all those expectations and then chooses how to investigate each suspected failure. Source-only code reviews catches static issues in the implementation but even a clean review leaves a good chunk of behavioral only issues untested. Unit tests, integrations, E2E, static analysis, runtime experiments and formal verification are all means to establish that behavior thereby generating different kinds of evidence and guarantees. This is why we believe a dedicated verification harness that can think and reason through all these modalities and invariants is necessary on top of general intelligence. The harness needs to start with the system’s intended behavior, develop a series of potential failure scenarios and choose how to investigate them. It’s sole functionality is to pressure test and challenge the assumptions behind a change, create the conditions needed to test suspected failures and assess what the resulting evidence establishes How Canary works: it takes a cold snapshot of the codebase when called, combining the supplied intent and team knowledge with requirements, decisions, prior issues from tools like Notion, Linear. It can also route questions to you through the coding agents if anything is ambiguous. Canary’s harness coordinates agent swarms by leveraging the different strengths across model families. It compares the code before and after, traces the effects through callers, dependencies, state transitions etc. and each suspected failure becomes a concrete scenario with an actor, state, trigger, outcomes and many more runtime states., For each suspected failure, Canary chooses the best way to provide evidence through methods like runtime verification, static analysis, unit, integration or sometimes even combination of these as necessary. The agent executes these checks in remote sandboxes by seeding data, configuring permissions, mocking dependencies and third party integrations and much more. Canary then returns these findings and supporting evidence back to the coding agents which then fixes these failures and requests reverifications against the failed scenarios. To get started, give your coding agent this setup instruction and tell us what it caught and how we can do better. Install the Canary CLI with npm i -g @runcanary/cli, then run canary skills and follow its instructions to onboard this repository. We are still pretty early in our journey and would love feedback on the product and how we can do better. https://ift.tt/EWiU2Pw September 25, 2026 at 12:57AM

Show HN: Offline recorder and transcriber on your MacBook's notch https://ift.tt/ucX5n4k

Show HN: Offline recorder and transcriber on your MacBook's notch Was dogfooding a version of this until a friend saw this and said I should probably productize this since it was more accurate than other commercial transcribers like Whisper. https://ift.tt/b9dLsFo September 25, 2026 at 12:01AM

Show HN: Radix – Visual UI for agentic programming https://ift.tt/XHrhT3R

Show HN: Radix – Visual UI for agentic programming Hey HN, I'm Jordan from Radix. Radix is a UI tool for programming agents. You prompt your agent to generate a workspace for a task you're working on and get an interactive widget that persists locally on disk. I built Radix because I always needed tools while I was writing code: tools to test little experiments, to play around with UI, to visualise results etc. Sometimes these would end up as python tools that read and plotted data, or separate React apps spun up just to test a single feature. Lately obviously I've been using Claude artifacts but these are quite limited. I wanted a system where it was easy to leave comments or adapt an artifact without having to give vague descriptions back to the agent, and where ideally I could actually shift away from a chat window as the main interface. I've got a pro version of Radix which I will launch soon. This current version is entirely free and is definitely a beta -- there will be rough edges! Note: There's no telemetry or data captured. Your messages run entirely through your own agent. I require a key but that's just to get an idea of how many people are using it. Everything is stored locally (the workspaces are actually just React apps which you can edit "artisanally" if you want). If you have any questions please reply here or email me hello@radix-os.com Thank you! https://radix-os.com September 24, 2026 at 10:35PM

Show HN: Open Jev Playground – try all the open source alternatives to Jev https://ift.tt/dn4ZpIf

Show HN: Open Jev Playground – try all the open source alternatives to Jev https://ift.tt/oj0rFug September 23, 2026 at 10:05PM

Show HN: AgentRun: DSL to turn agents into Workflows https://ift.tt/dluk9wC

Show HN: AgentRun: DSL to turn agents into Workflows Hi HN, I just open sourced the DSL that our harness in grep.ai uses to turn repeatable parts of agent work into workflows. You can combine tool calls, code, Jev-powered system one decisions for things like routing and screening evidence, and agents when a step needs more investigation. Our harness uses the traces and retro notes agents leave behind when doing a job to figure out which parts can become a workflow. The idea is to make the work easier to understand and avoid paying for a full agent loop where one isn’t needed. For example, a research workflow can split a question into subquestions, send agents to research them in parallel, use Jev to screen the evidence, and have another agent write the report. You can inspect the steps, evaluate the evidence screening separately, or change one agent without rebuilding everything. The DSL and examples are in our GitHub. There’s a scripted demo you can run without API keys: https://ift.tt/vIlDE6e You can also use it as a Pi extension to build, inspect, and run workflows: https://ift.tt/nyx1j4z I would love to hear if this is useful to others. More background on how AgentRun works in this video: https://www.youtube.com/watch?v=vOVhtGjtwpg . Or read about our use cases in this article: https://ift.tt/x6rDuyi . https://ift.tt/vIlDE6e September 23, 2026 at 11:42PM

Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes https://ift.tt/ehuoaFl

Show HN: I built a post-mortem debugger for native Windows x64/x86 crashes Hello HN! I've spent years debugging Windows crashes with tools that were either friendly but limited (e.g. Visual Studio) or powerful but archaic (e.g. WinDbg). I developed patterns and methods for understanding what was going on, and decided to build it into a much more effective debugging tool called ForensicDbg. I built a modern interface to minimize the friction when debugging. All of the data shown to you is analyzed, interpreted, and presented to you clearly, so you can focus on what matters. Everything is interlinked so you can quickly and intuitivly navigate through the process space. ForensicDbg comes with an MCP server which allows for agenic debugging. The work done to interpret and interlink your data also benefits AI tools. It removes the risk of hallucinations while building a stable foundation for them to work from without spending tokens. If you want to try it out you can sign up and get a free beta license here: https://ift.tt/GvfW30U https://ift.tt/683qu2b September 23, 2026 at 11:15PM