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Codex scraped the ICM website and discovered 2026 Fields Medal winner list

Codex starts encrypting prompts, uses ciphertext for inference instead

Show HN: Rejourney – Open-source revenue leak prediction for web and mobile apps Hello there (General Kenobi&#x27;s favorite way to start the conversation),<p>I’m Rashid, a sophomore at UT Austin. I built Rejourney.co (<a href="https:&#x2F;&#x2F;rejourney.co&#x2F;" rel="nofollow">https:&#x2F;&#x2F;rejourney.co&#x2F;</a>) to predict issues with your apps and websites before they happen, based on real user session recordings.<p>Here is a silly video (with cats) on how it works: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=Z95MDxBXMjk" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=Z95MDxBXMjk</a><p>It’s open source, and the post link is the github repo, but here it is again: <a href="https:&#x2F;&#x2F;github.com&#x2F;rejourneyco&#x2F;rejourney" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;rejourneyco&#x2F;rejourney</a><p>I originally built this because I had a campus freebie finder app that grew quickly, and I had a lot of users dm me on instagram about issues with the app’s onboarding and UX confusion. I initially lost about 340 users out of my 5,000ish users because of these issues, and I had to recover some by nudging them with notifications. It was a big pain, and I felt bad that I lost this many users to small and easy fixes. So I built Rejourney to predict that before it happens. Here is how it works:<p>First, the SDK is installed on Web JS, Swift, or React Native apps. You then help the SDK a little with a few lines of tracking important events -- such as a subscription bought, a signup completed, etc -- before you ship the app. We called these “critical conversion events”.<p>From here, Rejourney records the user session along with the meta data you set up, and relates it to the sequence of the user journey, each touch&#x2F;scroll&#x2F;pan interaction, and rage taps. If deemed an issue, it bundles in API response times and codes, ANRS, and crash traces into the context.<p>A heuristic then bundles all the user recordings into similarity cohorts for processing, and finds similar user journeys and outcomes in relation to the critical conversion actions that matter to you. If a trend is found that is possibly worrying, it admits the user recordings into segmentation and processing by an LLM on our back (in this case Gemini for cost and speed, but it has been tested on GPT 5.5 if you decide to self-host and set this up on your side).<p>If the LLM views similarities in the touch sequence frame by frame, it can determine whether the cohort is likely to present a negative outlook on the critical conversion event that matters to you. Based on the replays and all the surrounding context, it outputs a .MD file with the context and the fix that would patch it (which you can copy into your coding agent). Optionally, you can attach your github repo so the .MD file includes a code fix with the detected issue.<p>Furthermore, this occurs at the scale of thousands of user recordings daily. We have seen how this works on a medium-scale, as Rejourney has been tested with about 2.5 million user recordings from people shipping the SDK. One of our users even emailed us reporting a 30% increase in onboarding after 2 weeks of fixing non-stop issues found.<p>We have made it soooo cost effective to run with different strategies, that our first 3 paid users made us break even on costs…and this means more compute space for cool things later :D<p>Other considerations and criteria: Privacy was also very very important as we have to consider GDPR, after the retention period (usually 7-days) we quantize all the user recordings, anonymize all the fingerprints and aggregate them into a general dashboard (similar to Firebase’s general analytics dashboard).<p>I’d love to hear your feedback, critics, and requests in the comments! I’m all ears (or eyes since I’m reading).

The Future Worth Building Is Human – Thinking Machines Lab

The great digital fatigue: How digital burnout is changing social media use

European "age verification" "app" forcing everyone to use Android or iOS

Show HN: Benchmark your eng team's AI agent maturity in 5 minutes we had hundreds of discussions with engineering leaders over the past few months, and everyone&#x27;s trying to understand where they are in the AI journey.<p>we collected all this data into a benchmark and built a free grader to let you know where you stand.<p>you answer on a 1–5 scale (e.g., autonomy runs from &quot;suggestions only&quot; to &quot;agents own multi-hour workflows across code, infra, and external systems&quot;) - takes about 5 minutes.<p><a href="https:&#x2F;&#x2F;agent-benchmarks.com&#x2F;software-factory&#x2F;" rel="nofollow">https:&#x2F;&#x2F;agent-benchmarks.com&#x2F;software-factory&#x2F;</a><p>waiting for your results!

Indian scientists produce most detailed 3D atlas of the human brainstem

Just Let Me Write Digits

Australian energy retailers must provide three hours of free daytime electricity

YouTrackDB is a general-use object-oriented graph database

Building Food Metadata with LLM Juries

MorphoHDL: A minimalistic language for growing circuits

The Git history command deserves more attention

Show HN: Microphone – Talk out your side-project ideas, then test them with ads If you are an aspiring founder, any VC will ask you this question: “why are you the only person who could solve this”. If you want to generate passive income with your side idea, get ready to enter a crowded market as everyone and their mother is shipping. Unless you have an active X account or you’re a TikTok sensation distribution is going to be tough.<p>I just launched the trie.dev microphone beta to help folks find their edge. You yap into your phone about your ideas; Trie turns the rambling into hypotheses, then prioritizes them based on your experience and your realistic ability to distribute in that idea space — surfacing the problems only you can solve. From there you can generate creative and run Meta ads against your hypotheses straight from your phone, with zero setup, to see how real people respond.<p>I built it initially for myself and friends as an “intake form” for running paid ads to help validate our side gig ideas.<p>Happy to chat about how it works or the stack. Joining the waitlist will send you an email to join via TestFlight.

Show HN: FixBugs – Reproduce production bugs and verify fixes I built FixBugs, an agent that ingests the rich context surrounding production bugs to reproduce them in a sandbox and generate verified fixes. It&#x27;s available in the form of a self-hosted VSCode extension and as a Github app:<p>VSCode Extension: <a href="https:&#x2F;&#x2F;fixbugs.ai&#x2F;go&#x2F;vscode-extension" rel="nofollow">https:&#x2F;&#x2F;fixbugs.ai&#x2F;go&#x2F;vscode-extension</a><p><pre><code> - full code and data privacy. - zero data retention models opted out of training. </code></pre> GitHub App: <a href="https:&#x2F;&#x2F;fixbugs.ai&#x2F;go&#x2F;github-app" rel="nofollow">https:&#x2F;&#x2F;fixbugs.ai&#x2F;go&#x2F;github-app</a><p><pre><code> - we do access your code temporarily. - pick a repo to install FixBugs on. </code></pre> What motivated me to build FixBugs were my years being on-call at Google and VMware. How many hours did I spend gathering logs, traces, reviewing metrics, and reading code only to find that,<p>* Some context was missing.<p>* The bug wasn&#x27;t reproducible.<p>* The alert was caused by a transient infrastructure issue.<p>Too many. Inefficiency in investigating staging&#x2F;production bugs has a real cost, and it&#x27;s paid both by developers and customers.<p>Current capabilities:<p><pre><code> - Reproduce the bug. - Identify the root cause. - Generate a fix. - Verify the fix. - Review the generated code using multiple AI models to help catch potential regressions. </code></pre> Do try it and let me know what you think!<p>I&#x27;d especially love feedback from engineers who work with distributed systems or handle high-volume production bug triage.

What are Forward Deployed Engineers, and why are they so in demand? (2025)

Show HN: Sx 2.0 – Share AI skills with your team through a Dropbox folder Hi all, author here. SX started as a CLI to let developers share skills across AI clients without having to rely on git for storage. This allowed sharing at the Repo&#x2F;Team&#x2F;Org and Personal level.<p>However, the more we spoke to users the more we realized that non-technical users were actually using skills more and more but they had no way to share. And there was no way you were going to get your legal team to install and learn git.<p>SX 2.0 is targeting non-technical teams by adding a native Mac, Windows and Linux app. Our vault format was reworked so it can be used directly as a claude or codex plugin. And by storing your vault in Dropbox, Google Drive, iCloud or others you can be up and sharing in under a minute.<p>2.0 also adds an extension system with extensions that manage Skill Evals, LLM de-duping, metics and much more <a href="https:&#x2F;&#x2F;github.com&#x2F;sleuth-io&#x2F;sx-extensions" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sleuth-io&#x2F;sx-extensions</a>.<p>It&#x27;s Apache-2.0 and you can download it here <a href="https:&#x2F;&#x2F;github.com&#x2F;sleuth-io&#x2F;sx" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sleuth-io&#x2F;sx</a>.

Show HN: ContextVault – Shared memory layer for your AI and your team Hi HN,<p>I&#x27;m Kevin. I built ContextVault because I kept running into the same problem with AI tools.<p>Every project accumulated prompts, coding conventions, architectural decisions, examples, and other pieces of context that made the models significantly more useful. The problem was that this information quickly became fragmented. Some lived in ChatGPT Projects, some in Claude, some in Markdown files, some in internal documentation, and some only existed in previous conversations.<p>Late last year, I realized several people on our team were solving the same problems independently because previous work was difficult to discover. I assumed this problem existed in other large organizations, so I started experimenting with a shared context store.<p>I started with a local proof of concept and a rough MCP server. If I asked questions like &quot;have we done this before?&quot;, the AI could search the database and find the most relevant item to review. If a conversation produced something worth remembering, I could say &quot;save what we learned to the vault.&quot; After using that workflow for a few months, I found myself relying on it every day.<p>I decided to make it available to others. I&#x27;ve never built a product before, and I thought it would be a valuable learning exercise to do.<p>ContextVault is a a product for storing and organizing reusable context that can be shared across people, projects, and AI tools. Instead of copying the same instructions into every conversation, you can store them once and retrieve them through our MCP server. It is not limited to any one AI client. Your team can use ChatGPT, Codex, Claude, and Gemini and save&#x2F;read from the vault all the same.<p>It currently supports:<p>- OAuth support for GitHub, Google, Microsoft, and GitLab<p>- Structured context records with metadata<p>- Multi-user organizations with role-based access<p>- MCP server for all AI clients that support MCP<p>- Organization-scoped storage keeps tenant data separated<p>- Group visibility rules decide which memories each member can search<p>- Authenticated MCP access ties every request back to a real user and workspace<p>- Feedback signals can be captured now and used to improve ranking later<p>- Supports desktop versions of AI clients, not just their CLI versions (mobile app support should also work)<p>The backend is built with PostgreSQL, pgvector, Node.js, and TypeScript. The frontend uses Next.js, React, Tailwind CSS, and shadcn&#x2F;ui (frontend is not my strong suit, please be kind). Authentication is handled with Clerk and billing with Stripe.<p>I started building this for my own workflow, but after relying on it for several months I decided to make it available to others. We soft launched a few weeks ago, and I find it useful as a daily tool.<p>Essentially, ContextVault offers a way to track memories and context, distribute them instantly to your team, and help reduce duplicated work.<p>I&#x27;d be interested in feedback on a few things:<p>- How are you managing reusable AI context today?<p>- Are you relying on similar tools, or do you keep everything in Git or Markdown?<p>- If you&#x27;ve built something similar, what did you learn that you would do differently?<p>You can see the product here:<p><a href="https:&#x2F;&#x2F;www.contextvault.dev" rel="nofollow">https:&#x2F;&#x2F;www.contextvault.dev</a>

A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI