Evidence-first research

[OS] Mac Sai: a free and open source Mac cleaner and optimizer, no telemetry, no subscription

Quick disclosure so this is above board: I'm the creator and main maintainer, It's free and open source, no catch, and I'll be upfront about the weak spots too. Like a lot of you I've used CleanMyMac. Good app. I just didn't love paying a yearly subscription for something I reach for a few times a year. So a while back I started building a free, open source alternative: I took the handful of tools I actually use (from CleanMyMac, OnyX, Pearcleaner and others) and put them in one native app. It's called Mac Sai. A few of you might remember it as Mac Clean; same project, I just renamed it. It recently **passed 1.6k stars and 90 forks**, which is honestly wild to me, so it felt like time for a proper write-up. **The skeptical part first, because it's fair** * You probably don't *need* a Mac cleaner. macOS tidies up after itself fine. This is for when you *want* to reclaim space quickly, wipe the leftovers an uninstalled app leaves behind, hunt duplicates, or see what's pinning your CPU. * Plenty of "cleaners" are snake oil or actively harmful. So this one is deliberately boring about safety: it moves files to the Trash instead of hard-deleting, it refuses to touch SIP-protected and system paths, and you review and confirm everything before anything is removed. There are almost 900 automated tests (879 today), and a lot of them exist purely to guard the deletion paths. * The malware scan is honest about what it is: a curated list of known adware and bundleware families plus a few

Obscura: The first VPN that can't log your activity

Ask HN: Which is cheaper: ChatGPT tokens or a 2nd Pro 20x subscription?

My usage ran out for the remainder of the week. I am tempted to buy tokens but I remember how overpriced they were back when I used Claude.

Show HN: AI·rete·RAG – a Rete rule engine decides, RAG explains why

Hi HN, I built ai·rete·rag because I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact. It runs the two in series instead: 1. A pure-Python Rete engine evaluates YAML rules against your facts. The verdict comes only from here. Same facts, same verdict, every time, with salience-based conflict resolution. 2. RAG retrieves passages from your own policy documents, and an LLM writes a plain-English explanation of the decision that was already made, citing those passages. It can't change the verdict. A few things that went further than I expected: - Rules are a graph, not flat lists: nested all/any/not, and rules can assert facts that other rules consume (forward chaining). The decision trace shows the causal chain. - Audit mode records every rule evaluated, including the ones that didn't fire, condition by condition, with a snapshot of the rule set for replay. - Rules can steer retrieval (a fired rule narrows which documents get searched), and retrieved text can be turned into facts for the engine. - Non-technical authors can build rules in a visual editor, or paste a policy document and get LLM-drafted rules with citations. Drafts are never saved without review. YAML is still there for engineers. The landing page has a live demo with no signup (8 demo domains: loan, fraud, clinical, insurance, legal, ops, e-commerce, blockchain). There's also an MCP server, so Claude

Congratulations to r/macapps for reaching 250,000 users!

Congrats to us! In January 2026 I made [this post for 200,000 joined users](https://www.reddit.com/r/macapps/comments/1qbrcg5/congratulations_to_our_rmacapps_for_reaching/), 8 months later, we hit 250,000! Shout out to the moderators who are doing a great job at keeping the sub clean and focused, the recent [PCP changes phase 3](https://www.reddit.com/r/macapps/comments/1ryaeex/rmacapps_mods_went_too_far_whats_changing_phase_3/) and [megathread](https://www.reddit.com/r/macapps/comments/1w4brkd/megathread_the_app_pile_september_2026/) changes were a huge improvement to the sub. Shout out to the users, reviewers and developers and everyone keeping the good vibes (NOT VIBE CODED, JUST GOOD VIBES) in the sub ❤️ That's it, you're all beautiful, see you in @ 300,000. (FYI if you want to see the users subscribed to a sub, go to a related sub like r/apple or r/macOS and see "MAC SUBREDDITS" side panel, currently we're at 251,488) Cheers 🍻

Show HN: InstinctFlash – High-Performance Serving Runtime for Robotics Models

Hey HN, Guanming here, cofounder of General Instinct. We just released InstinctFlash, a high-performance serving framework for robotics models. It’s licensed under AGPL-3.0. On Jetson Thor, we see speedups about 1.2x to 7.9x from runtime optimizations alone and up to 33.78x for LingBot-VA when we combine those runtime optimizations with a distilled few-step diffusion scheduler, going from the original 25 visual / 50 action steps to 2 / 4 steps. Across 50 Robotwin2.0 tasks, we evaluated 1,153 episodes per configuration, LingBot-VA with InstinctFlash at 2 visual / 4 action steps achieved a 90.5% success rate, compared with 92.1% for the baseline at 25 visual / 50 action steps. Here’s an optimized 5B world action model, running in real time on a Jetson Thor: https://youtu.be/nku65iyL5Fw InstinctFlash currently supports 8 VLA / world-action model families, including pi0.5 and NVIDIA Cosmos Policy, across RTX 4090 / 5090 and Jetson Thor. Just give it your fine-tuned checkpoint and InstinctFlash handles the rest, exposing the accelerated model through a Python runtime or an OpenPI-compatible WebSocket server. We started working on this because we kept running into the same problem while deploying robot policies, the models were getting much better, but inference was often way too slow for the control loop we actually wanted. For pi0.5, mixed-precision GEMMs and CUDA graphs speed up computation and reduce launch overhead. For Cosmos, cach

My current essentials for macOS

***TL;DR:*** My essential apps are already shown on the picture above, if you prefer to just take a quick look. Then, if you're interested in a category in particular and why my choices are those, just feel free to scroll down and read. My criteria is: 1. No subscription based apps, unless they fall into a) the *hybrid subscription* model, like with GoodLinks or Escape - Mind Maps, or b) the free tier doesn't constrain my usage, like with Unread or I love PDF. 2. If there's any sort of AI involved, it must be on-device. 3. Performance, efficiency, and good usability. 4. Mac App Store versions are always preferred, and most of the apps are from there. 5. Ideally, with iOS and iPadOS versions and iCloud sync across them. 6. Little to no telemetry. I've observed all the apps running different Firewall apps, and I've discarded those that, despite being really good, send to many outgoing connections, such as the Flaschards app Mochi, which makes LOTS of outgoing connections to different servers and IPs. 7. No vibe-coded apps. Although most apps nowadays are greatly assisted by AI, especially those from solo developers, it is quite noticeable when an app has been mainly vibe-coded, or the developer has experience and knowledge, and AI has been used just for repetitive tasks. **Introduction:** This is a compilation of my must-have apps. Those that I first install on my Mac once I have formatted and made a clean install. Those aren't all the apps that I will end up installing (althou

Ask HN: Should we still learn coding?

Paul Graham talked about this in a recent essay: "Should students still study computer science if AIs will write most code? ... even if you have AIs writing all your code for you, you're still in the position of an engineering manager, and good engineering managers should be able to do the work of those working for them." Paul Graham. August 2026 https://paulgraham.com/prepare.html I think that argument makes a lot of sense and coincides with my experience. I code all my projects, and I only use AI as a teacher, or bug finder. Again and again I see AI giving me a complicated, confusing code for something that could be much simpler, cleaner and organized. If I let AI write code I do not completely understand, sooner than later it all becomes a great mess that not even that AI can understand. Still there are a lot of people who say that learning to code is not necessary. What do you think - Should we learn to code? - If you already know how to code, do you keep learning? - What are your experiences?

Congratulations to r/macapps for reaching 250,000 users!

Congrats to us! In January 2026 I made [this post for 200,000 joined users](https://www.reddit.com/r/macapps/comments/1qbrcg5/congratulations_to_our_rmacapps_for_reaching/), 8 months later, we hit 250,000! Shout out to the moderators who are doing a great job at keeping the sub clean and focused, the recent [PCP changes phase 3](https://www.reddit.com/r/macapps/comments/1ryaeex/rmacapps_mods_went_too_far_whats_changing_phase_3/) and [megathread](https://www.reddit.com/r/macapps/comments/1w4brkd/megathread_the_app_pile_september_2026/) changes were a huge improvement to the sub. Shout out to the users, reviewers and developers and everyone keeping the good vibes (NOT VIBE CODED, JUST GOOD VIBES) in the sub ❤️ That's it, you're all beautiful, see you in @ 300,000. (FYI if you want to see the users subscribed to a sub, go to a related sub like r/apple or r/macOS and see "MAC SUBREDDITS" side panel, currently we're at 251,488) Cheers 🍻

Show HN: InstinctFlash – High-Performance Serving Runtime for Robotics Models

Hey HN, Guanming here, cofounder of General Instinct. We just released InstinctFlash, a high-performance serving framework for robotics models. It’s licensed under AGPL-3.0. On Jetson Thor, we see speedups about 1.2x to 7.9x from runtime optimizations alone and up to 33.78x for LingBot-VA when we combine those runtime optimizations with a distilled few-step diffusion scheduler, going from the original 25 visual / 50 action steps to 2 / 4 steps. Across 50 Robotwin2.0 tasks, we evaluated 1,153 episodes per configuration, LingBot-VA with InstinctFlash at 2 visual / 4 action steps achieved a 90.5% success rate, compared with 92.1% for the baseline at 25 visual / 50 action steps. Here’s an optimized 5B world action model, running in real time on a Jetson Thor: https://youtu.be/nku65iyL5Fw InstinctFlash currently supports 8 VLA / world-action model families, including pi0.5 and NVIDIA Cosmos Policy, across RTX 4090 / 5090 and Jetson Thor. Just give it your fine-tuned checkpoint and InstinctFlash handles the rest, exposing the accelerated model through a Python runtime or an OpenPI-compatible WebSocket server. We started working on this because we kept running into the same problem while deploying robot policies, the models were getting much better, but inference was often way too slow for the control loop we actually wanted. For pi0.5, mixed-precision GEMMs and CUDA graphs speed up computation and reduce launch overhead. For Cosmos, cach

My current essentials for macOS

***TL;DR:*** My essential apps are already shown on the picture above, if you prefer to just take a quick look. Then, if you're interested in a category in particular and why my choices are those, just feel free to scroll down and read. My criteria is: 1. No subscription based apps, unless they fall into a) the *hybrid subscription* model, like with GoodLinks or Escape - Mind Maps, or b) the free tier doesn't constrain my usage, like with Unread or I love PDF. 2. If there's any sort of AI involved, it must be on-device. 3. Performance, efficiency, and good usability. 4. Mac App Store versions are always preferred, and most of the apps are from there. 5. Ideally, with iOS and iPadOS versions and iCloud sync across them. 6. Little to no telemetry. I've observed all the apps running different Firewall apps, and I've discarded those that, despite being really good, send to many outgoing connections, such as the Flaschards app Mochi, which makes LOTS of outgoing connections to different servers and IPs. 7. No vibe-coded apps. Although most apps nowadays are greatly assisted by AI, especially those from solo developers, it is quite noticeable when an app has been mainly vibe-coded, or the developer has experience and knowledge, and AI has been used just for repetitive tasks. **Introduction:** This is a compilation of my must-have apps. Those that I first install on my Mac once I have formatted and made a clean install. Those aren't all the apps that I will end up installing (althou

Ask HN: Should we still learn coding?

Paul Graham talked about this in a recent essay: "Should students still study computer science if AIs will write most code? ... even if you have AIs writing all your code for you, you're still in the position of an engineering manager, and good engineering managers should be able to do the work of those working for them." Paul Graham. August 2026 https://paulgraham.com/prepare.html I think that argument makes a lot of sense and coincides with my experience. I code all my projects, and I only use AI as a teacher, or bug finder. Again and again I see AI giving me a complicated, confusing code for something that could be much simpler, cleaner and organized. If I let AI write code I do not completely understand, sooner than later it all becomes a great mess that not even that AI can understand. Still there are a lot of people who say that learning to code is not necessary. What do you think - Should we learn to code? - If you already know how to code, do you keep learning? - What are your experiences?

Show HN: Drop – A rootless Linux sandbox with gVisor support

I created Drop because I always felt uneasy installing and running third-party programs using my main user account. A single compromised dependency means a full compromise of the system. What is even worse, because I ship software from my computer, a single compromised dependency can lead to compromise of all the users of my software. Containers and VMs are one solution, but for local work, they are often detrimental to productivity. It takes effort to configure a machine with all the tools and configs needed for productive work, but a container or a VM will be stripped of all these tools. This is great for production deployments, where the aim is a reproducible system with minimal dependencies, but can get in the way of productive local work. Drop is language independent, but the workflow is inspired by Python's virtualenv. With virtualenv the environment isolation is only a convention that relies on installed dependencies being good citizens. With Drop the isolation is enforced. Each Drop environment gets its own writable and easily disposable home dir, with only selected config files and dirs from the original home mounted, mostly read-only. Drop uses Linux namespaces for isolation (user, mount, network, PID, IPC, cgroup), doesn't require root and, as an option, uses gVisor user-space kernel, which adds protection against exploiting host kernel vulnerabilities to escape the sandbox. I don't want to make the introductory post too lengthy, but I'm here to answer any question

StepGrab 1.5: record a task on your Mac, get a written guide as PDF, HTML, Markdown, GIF or MP4. Native, offline, now with redaction

I'm Julian, I study business informatics and I work on StepGrab next to uni. It's on the Mac App Store. I posted here back in July and most of what's in it now came out of that thread. PROBLEM At my working student job I was the guy people asked when they didn't know where some setting was, or which button comes after which one. Every time the same thing: fifteen screenshots, crop them all, red arrows in Preview, type the steps underneath. Half an hour or more per guide, and three weeks later some of it was wrong again anyway. StepGrab does that part now. You hit record, do the task once like you normally would, and afterwards every click is a screenshot with a numbered arrow and a sentence saying what you did there. Then you export it as PDF, HTML, Markdown, GIF, MP4 or a portrait video. It's a native Mac app, no Electron, runs on macOS 14 and up. Nothing gets uploaded, there's no account, and the sentences come from Apple's on-device models. The last two updates added: * an editor. Grab the number on an arrow to change direction and length, grab the middle to bend it. You can rewrite any sentence, reorder steps, delete them, ⌘Z works everywhere. Every format has a preview that renders the real output, not a mockup * redaction, which was the most asked for thing by far. Drag a box over a name, an email, a licence key. That bar is rendered into every export, also into what you copy or drag into Slack, so there's no layer on top that somebody can pull away afterwards * shapes,

Show HN: JevBench, a reproducible benchmark for typed decision models

Hi HN! I built JevBench because Jev kicks ass, and the world deserves to know how the serious open source and fake lookalike projects really perform in comparison. Jev-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on. JevBench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting. A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost. Leaderboard right now: #1 - Jev 74.4 #2 - SemIf 73.1 #3 - djev 73.0 #4 - Winnow-12B Q8 71.2 #5 reflex 4B 70.3. MIT harness, public items, frozen artifacts, scoring code and public per-task outcomes: https://github.com/fstandhartinger/jevbench Two no-signup demos: https://who-is-right.app.mintapis.com https://is-it-ai-slop.app.mintapis.com Limitations: English-only; latency from one German server; local/demo latency gets a disclosed ×2 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise. Wdyt?

[OS] I built Klippy, a free and private clipboard manager for macOS

I built Klippy because I wanted clipboard history that I could actually search and organize without sending everything I copy somewhere else. **Problem** I copy a ridiculous amount of stuff every day. Links, code, images, files, emails, JSON, colours, random text, and things I know I'll need again later. A basic list of recent clipboard items works until you're trying to find something you copied days or weeks ago. So I built Klippy. Everything you copy goes into searchable history, and Klippy automatically sorts it by what it is. You can search by type, date, or source app, pin things you use often, save clips, merge multiple clips, and bring Klippy up from anywhere with a keyboard shortcut. Everything is stored locally on your Mac. No account, no server, and no analytics. Klippy also skips clipboard items that password managers mark as concealed and doesn't require Accessibility, Screen Recording, or Full Disk Access. **Comparison** I looked at clipboard managers like Maccy and Copy 'Em. Maccy is great if you want something minimal and fast, while Copy 'Em offers a broader set of clipboard features. With Klippy, I wanted something in between with more focus on organization and search. It automatically recognizes different types of content, lets you filter by type, date, and source app, and includes pinning, saved clips, merging, keyboard recall, and customizable skins. It's also completely open source, so you can see exactly what a clipboard manager that has access to every