Survivor.io Calculator — 《弹壳特攻队》伤害与升级计算器，用统一参数比较装备、技能和升级方案

@BlackLedgerSig · X

fathermarz · HN

I’ve worked in software supply-chain security for six years doing binary analysis on firmware, industrial controls systems, and commodity IT closed/open source software. What I learned is that in hopes to secure the software supply chain we have ran a very similar playbook and mostly over engineered the problem, with SBOMs and VEX documents and expected the industry to comply. But what was discovered was that every SBOM looks different depending on who produced it and at what step in the chain,

u/Zealousideal_North83 · Reddit

Fidibeki — Product feedback that finishes the job Building products is what drives me. While working on my own products, I noticed the same problem: feedback comes in, loses context, and users rarely hear what happened next. So I built Fidibeki. Users can report issues directly inside a product with screenshots, audio, or session replay. I can triage everything in one inbox, merge duplicates, track progress, and keep reporters updated. Privacy is built in: session replay

gojongo · Product Hunt

ReWeaver AI DriftDetector Drift score for any GitHub repo

@TCVarbanov · X

Building ReplyGauge 👇 Making customer support quality public and measurable for software products.

u/DetlefRichter · Reddit

Made a prototype: EU AI Act Self-Assessment Tool. Worth turning into a product? Made a prototype over the weekend. Short questionnaire that classifies your AI system under the EU AI Act into one of five tiers (Prohibited, High-Risk Annex I, High-Risk Annex III, Limited-Risk, Minimal-Risk), with a plain-language explanation of your compliance deadline and next steps. The trigger: I keep seeing founders with AI features who have no idea whether they even fall under the Act, and legal advice i

@iamjackwiz · X

victor_edka · HN

HRAG – Hybrid RAG on €116/month of Hetzner, officially benchmarked

u/dumbfoundded · Reddit

Ito, AI Code Review that Runs Code I've been using AI code review tools but none of them actually run code so I built one: https://www.ito.ai/ The way it works is that it uses microVMs to spin up your environment with all of the services running. Then a bunch of AI agents go and test the application to collect runtime evidence. The result is you get test cases along with evidence about whether or not the test cases pass or fail. The runtime evidence can be videos, request/response curls, db

u/inc_23 · Reddit

Hey, I created a tool that catches when your LLM is confidently wrong, in production, in real time — looking for beta testers. Your bot sounds sure of itself even when it's wrong, and you usually only find out when a customer complains. Auscope audits every LLM response in the background: 3 models from 3 different providers independently check it, a 4th "chairman" model resolves disagreements, and you get one verdict — verified, uncertain, or unreliable. Runs async, doesn't slow your respon

cansofgrease · HN

Pingularity – scheduled Ookla/iperf3 speedtest dashboard, outage alerts