Show HN: WineBar: A yet another Wine prefix manager, with Asahi Linux support https://ift.tt/yTUc2KC

Show HN: WineBar: A yet another Wine prefix manager, with Asahi Linux support My daily driver is a Macbook Air M2 running Linux - Fedora Asahi Remix to be precise. One thing I missed when using it is the ability to occasionally run Windows software using Wine. Apparently, you can run Steam on it and apparently Steam allows installing and running arbitrary Windows software, but when I tried it, I couldn't create an account and in general, I'd rather not use Stream. I succeeded running and older version of Heroic Games Launcher [1] under muvm (a virtual machine that runs a 4K page kernel on a 16K one). That wasn't terribly easy though and I wanted a better experience. My other problem with Heroic and with other launchers focused on games is the lack of flexibility - they either work for a particular piece of software or they don't, and you can't do anything about it. For instance, an installer may require a certain package to be installed using Winetricks [2], before it runs. Heroic doesn't give you a chance to run anything before the installer runs. Long story short, I decided to build my own Wine prefix manager that would be flexible, not focused exclusively on games and would run on Asahi Linux. Also, I decided to write it in a new language for me (Dart / Flutter) and learn that language as a byproduct. Five months later, it's finally ready and I'd like to get feedback on it. BTW, it also supports regular x86_64 Linux distros, though it doesn't receive as much testing on them. [1]: https://ift.tt/q6t2XAC [2]: https://ift.tt/6vyiWrk https://ift.tt/HTs7rqa December 13, 2025 at 06:36AM

Show HN: I audited 500 K8s pods. Java wastes ~48% RAM, Go ~18% https://ift.tt/Yi42TJZ

Show HN: I audited 500 K8s pods. Java wastes ~48% RAM, Go ~18% https://ift.tt/JydxTpX December 13, 2025 at 05:23AM

Show HN: Ten Principles of Good Design https://ift.tt/orPTyfa

Show HN: Ten Principles of Good Design https://ift.tt/Dw1jdRs December 12, 2025 at 06:13PM

Show HN: Autofix Bot – Hybrid static analysis and AI code review agent https://ift.tt/TdgLCDr

Show HN: Autofix Bot – Hybrid static analysis and AI code review agent Hi there, HN! We’re Jai and Sanket from DeepSource (YC W20), and today we’re launching Autofix Bot, a hybrid static analysis + AI agent purpose-built for in-the-loop use with AI coding agents. AI coding agents have made code generation nearly free, and they’ve shifted the bottleneck to code review. Static-only analysis with a fixed set of checkers isn’t enough. LLM-only review has several limitations: non-deterministic across runs, low recall on security issues, expensive at scale, and a tendency to get ‘distracted’. We spent the last 6 years building a deterministic, static-analysis-only code review product. Earlier this year, we started thinking about this problem from the ground up and realized that static analysis solves key blind spots of LLM-only reviews. Over the past six months, we built a new ‘hybrid’ agent loop that uses static analysis and frontier AI agents together to outperform both static-only and LLM-only tools in finding and fixing code quality and security issues. Today, we’re opening it up publicly. Here’s how the hybrid architecture works: - Static pass: 5,000+ deterministic checkers (code quality, security, performance) establish a high-precision baseline. A sub-agent suppresses context-specific false positives. - AI review: The agent reviews code with static findings as anchors. Has access to AST, data-flow graphs, control-flow, import graphs as tools, not just grep and usual shell commands. - Remediation: Sub-agents generate fixes. Static harness validates all edits before emitting a clean git patch. Static solves key LLM problems: non-determinism across runs, low recall on security issues (LLMs get distracted by style), and cost (static narrowing reduces prompt size and tool calls). On the OpenSSF CVE Benchmark [1] (200+ real JS/TS vulnerabilities), we hit 81.2% accuracy and 80.0% F1; vs Cursor Bugbot (74.5% accuracy, 77.42% F1), Claude Code (71.5% accuracy, 62.99% F1), CodeRabbit (59.4% accuracy, 36.19% F1), and Semgrep CE (56.9% accuracy, 38.26% F1). On secrets detection, 92.8% F1; vs Gitleaks (75.6%), detect-secrets (64.1%), and TruffleHog (41.2%). We use our open-source classification model for this. [2] Full methodology and how we evaluated each tool: https://ift.tt/5l47N0f You can use Autofix Bot interactively on any repository using our TUI, as a plugin in Claude Code, or with our MCP on any compatible AI client (like OpenAI Codex).[3] We’re specifically building for AI coding agent-first workflows, so you can ask your agent to run Autofix Bot on every checkpoint autonomously. Give us a shot today: https://autofix.bot . We’d love to hear any feedback! --- [1] https://ift.tt/d2h1UrS [2] https://ift.tt/yvBgrxk [3] https://ift.tt/by6A318 December 11, 2025 at 11:24AM

Show HN: Tripwire: A new anti evil maid defense https://ift.tt/gJU0mTZ

Show HN: Tripwire: A new anti evil maid defense If you have heard of [Haven]( https://ift.tt/tAuI2ZB ), then Tripwire fills in the void for a robust anti evil maid solution after Haven went dormant. The GitHub repo describes both the concept and the setup process in great details. For a quick overview, read up to the demo video. There is also a presentation of Tripwire available on the Counter Surveil podcast: https://www.youtube.com/watch?v=s-wPrOTm5qo https://ift.tt/Nr9nJcA December 10, 2025 at 11:46PM

Show HN: Workmux – Parallel development in tmux with Git worktrees https://ift.tt/053NxjW

Show HN: Workmux – Parallel development in tmux with Git worktrees https://ift.tt/aC0QVk5 December 12, 2025 at 01:03AM

Show HN: A minimum viable Markov gibberish generator in 32 lines of Python https://ift.tt/lbtBi9G

Show HN: A minimum viable Markov gibberish generator in 32 lines of Python https://ift.tt/EePgRpl December 12, 2025 at 12:23AM