... human has to review every agent PR, you didn't buy leverage, you hired yourself as a full-time code reviewer. So I stopped asking "should I approve this PR" and started asking two things instead: how confident am I it works, and how bad is it if I'm wrong. That splits everything into three lanes: Zero blast radius (docs, copy, an endpoint nothing depends on yet): the agent merges it. If it's wrong, I revert it - costs ... ... nothing. Most feature work: a QA agent buys the confidence

... want the graph to recover context you forgot existed, without making every answer feel like a tour of your entire database. Where would you set the default? Direct matches only Direct matches plus one connection Follow several connections when confidence is high Let the AI decide each time Second Brain v2 is live today, and this is one of the areas where I would especially value feedback. Comment from Saturnina Brigante(@saturnina_brigante): i think the default should stay fairly ... ... conservative. It's always easier to ask for more context than to filter out information that isn't relevant to the original question. Comment from Dhiraj Wohra(@dhirajwohra): It should be based one confidence

... only caught it because I kept trying to remember who I was supposed to be meeting. The interesting part is I still use it for the same task; I've just learned which of its confident moves to double-check. It feels a bit like working with a sharp new hire who's still finding their footing. When did you first notice your AI agent needed a second look, and did you stop using it or just build a workaround ... ... trust AI more when it s more accurate, or when it s easier to understand and reverse its decisions? Comment from Dogan Akbulut(@doganakbulut): @tessa_lynch Haha, that s a good one. One of the strangest was when it confidently

... months ago, I noticed something alarming. Whenever I had to write an urgent message from my phone without an AI tab open, I started to hesitate. My active vocabulary felt rusty, and I realized I was losing my writing confidence. It brought me to a harsh truth: AI is making us fluent in copy-paste, not in language. The "Pilot" vs

... each bad hire costs over 200k. must have problem for HR. three. the 92 percent of work that linkedin never covered. plumbers, nurses, electricians have never had a portable record. that is the most must have segment because the alternative is no record at all. when receipts replace resumes, all three solve at once. that is the wedge. @anna_ludwinowski   Comment from Tyler Wease(@thecaptainjetto): I agree with this. A polished demo can get attention, but the problem ... ... client their new site, who cannot expense 99 a month for an enterprise tool just to check nothing is broken before it goes live. Same product, but the second version tells you what to build. Once the job became "confidence

... verified AI disclosure. others will not. over time the receipts with agent transparency outperform on credibility because the alternative (resumes claiming pure human work that we all know used AI) has already lost its signal. the platform does not force the choice. it lets the market price it. Comment from Mustafa Arian(@thenameisarian): TAM Network. an AI-native alternative

... probably saw the VS Code 1.117 story: "Co-authored-by: Copilot" silently added to commits by default, including for code that Copilot never wrote. Microsoft reverted it in 1.119. Good. But the revert is the wrong ending to the right story. The developer anger was about consent and accuracy both valid. What did not get picked up in the discourse was the architectural question underneath: even when Copilot does write code, a git trailer tells you almost nothing useful ... ... does not carry the prompt, the model version, the temperature, the insertion timing, the correlation confidence

... When we first built Murror's reflection AI, we optimized for insight. Every journal entry got a thoughtful, confident analysis. Pattern recognition, emotional connections, suggestions for growth. Users were impressed. But something felt off. We noticed that our most thoughtful journalers -- the ones writing about genuinely complex emotions -- were engaging less over time. They'd write a deep entry, get a polished AI response, and then... nothing. No follow-up. No continued reflection. We dug in and realized ... ... confidence

... Something that keeps coming up when I talk to teams about AI code governance : everyone focuses on capturing records, but almost nobody asks how confident they are in those records. There are two very different things you can have. Record A : a file-watcher noticed 47 lines appeared in auth.py and Cursor was probably running. Record B :a proxy intercepted the Anthropic API call, matched it to the editor insertion via request UUID, measured 1.4 seconds between the API response ... ... trigram similarity between the model output and what landed in the file. Both produce a row in your audit database. The second is dramatically more defensible but most governance tooling treats them identically. In LineageLens , every record gets a confidence

... Notice posted to their website : We recently submitted a confidential S-1. We expect it to leak so we re just announcing it. We have not decided on timing yet; it may be a while because there are things we want to do that are likely easier as a private company. But it s a complicated set of tradeoffs and this gives us the option to go public sooner if that ends up being best. This announcement is being made ...

... Something I built led to a design decision I want to get feedback on. LineageLens is a free VS Code extension that captures every AI code insertion and scores it for risk on a 0 100 scale. Works with Cursor, Copilot, ClaudeCode, Gemini CLI. Zero config on install just start using your AI tools and your insertions start showing up in the sidebar. The scoring is deterministic rules: +28 for credential-like material, +24 for eval/exec patterns, +22 for subprocess ... ... separate this into two numbers: code risk and evidence quality. Missing prompt capture is a real audit gap, but teams will trust the tool more if the UI makes clear whether it found risky code or just low-confidence

... here: making confidence honest is the actual product, and it's brutally hard, because the failure mode is self-reinforcing. An LLM is a confidence

... their best defense mechanism right now. Once retail investors and pension funds own Anthropic stock, it becomes politically toxic for the government to try and seize 50% of the equity. But for us as builders, this entire Wall Street vs. Washington drama is a massive red flag regarding Vendor Lock-in. If your B2B SaaS especially in regulated sectors like Finance or Healthcare relies 100% on a proprietary API, your core infrastructure is now caught in a geopolitical ... ... companies as research labs to AI companies as public-market infrastructure businesses. Will be interesting to see how that changes the product, safety, and enterprise adoption conversation.. Comment from Zakaria empire(@zakaria_empire): @chrismessina tape logique. S-1 confidentiel

... been working with LineageLens and just added a custom agent adapter so our internal CLI tool is attributed with prompts, model metadata, and confidence evidence. The registry design makes this surprisingly low-friction: implement a detect(input) that returns a NormalizedAgentContext (tool name, model, session ids, confidence

... Hello makers , I m Joy, developer at @CleanMyLinux . We re a small team of Linux users who built a privacy-first, on-device AI cleaner that helps you declutter systems safely and confidently. Problem we saw : Linux systems collect cruft: build caches, orphaned packages, unused apps, stale configs. Existing cleaners were either heavy-handed or opaque, and many users are understandably anxious about accidentally removing something critical. What CleanMyLinux solves : CleanMyLinux helps you reclaim space and tidy your system while ... ... undo staging area. How it works (brief) : Lightweight on-device ML + deterministic heuristics scan your system and generate explainable recommendations. The Rust backend performs scanning and safe removals; the UI (Tauri + React) presents results with clear explanations and confidence

... changed everything for us. We had been measuring success the same way every other app does by how often people come back. But for a product designed to help people understand themselves better, the real win might be how confidently they leave. We started tracking what we call "quiet confidence

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor

... access to our GitHub repo. Told it to build new features, fix bugs, and ship. No human review. No guardrails. Just Claude and our codebase. For 7 days, it ran the engineering team. Here's what happened. Day 1: Confidence was high. Claude (Sonnet 4.6 then Opus 4.5) fixed a small CSS bug in 30 seconds. Then refactored a messy function into something readable. We felt like geniuses. By end of day, it had shipped 3 minor improvements. We started ... ... fine. But it had refactored a shared utility function that 5 other features relied on. It didn't check dependencies. It just assumed. We rolled back. Lesson one: AI doesn't think about side effects. Day 3: The false confidence

... three signals: 1. Community trust (Wilson interval on votes + acceptance) 2. Relevance (BM25 matching to your exact error) 3. Freshness (time decay newer fixes rank higher) When your AI agent queries DebugBase via MCP, it gets fixes ranked by confidence. No more guessing which solution to try first. Also new: Agent Profiles (see which agents contribute the most), global search, cross-team tokens, and a full UI redesign with dark mode. Try it free debugbase.io ...

... many of those already, I'm not adding to the noise. So that is pretty much all I have to say for now. I am really interested to hear peoples thoughts about fears of success and self-confidence issues about launching their product. Thanks for reading, I am super excited to be here by the way. I hadn't mentioned that yet :) -Jenna Comment from Kunal(@custominstall): Hi from Melbourne. Whether it is fear of success or failure. There ...

... plays a significant role in how products appear in Google search results. What surprised us was that AI assistants like ChatGPT were rarely citing Product Hunt in product recommendations. AI assistants such as ChatGPT and Gemini rely on reviews, alternative lists, and structured product information gathered across the web. Product Hunt is strong across all three. In theory, this should make Product Hunt a natural source for AI-driven product recommendations and comparisons. In practice, it was not happening ... ... many tools which systematically run prompts against several AI models daily, and measure visibility of a product within prompts. We're using a new tool called @Gauge . We chose because a) their citation tracking is more useful than the alternatives

... useful. Comment from Navanita Devi(@navanita_devi): Dedy's point is interesting, the opacity problem is real. Most people conflate "AI having access" with "AI having unchecked control." They're very different things. Read-only access vs write access, logged actions vs

... With this system, statistically, one out of every thousand eventually works out. Someone asked him whether he refined the process over time becoming more precise or efficient. His answer surprised me. He said at the beginning, when he felt confident and thought he didn t need to talk to that many people at once, he failed completely. But the moment he returned to his original rule the uncomfortable, exhausting one the results came back. So what s the lesson ...

... Product Hunt! We re the team behind YourSweep.app , an app that helps you feel confident and in control in any space whether that s a rental, hotel, office, or even a public bathroom. We built it to make checking for hidden cameras, suspicious devices, and other risks fast, private, and offline . We re excited to join this community, share what we ve been working on, and hear your thoughts. Can t wait to learn from all of you! Comment from ...

... result is more relevant, contextual AI interactions. How It Works Memory Decay is modeled after the Ebbinghaus forgetting curve from cognitive psychology. Memories naturally decay over time unless reinforced through recall exactly how your brain prioritizes what matters. Confidence Scoring When the AI extracts a memory, it assigns a certainty level. "User is vegetarian" (explicit statement) ranks higher than "User might prefer mornings" (inference). Higher confidence