Qwen 3.6 27B is the first local model that genuinely replaces a cloud coder.
What the article says
- The author ran Qwen 3.6 27B on an Apple Silicon laptop and found it the first local model worth using as a daily driver for coding tasks.
- It hits around 30 tokens per second on high-end Apple Silicon, which is within the range of cloud API response speeds.
- The dense 27B variant is slower than the sparse 35B version but produces better code, so the author prefers it despite the speed trade-off.
- Setup uses llama.cpp with an 8-bit quantized model from Hugging Face, which fits in 48GB of shared RAM and pairs cleanly with coding agents like OpenCode.
- Benchmarks show it ahead of Gemma 4 31B for coding, and the author sees it as a stepping stone toward truly frontier-level local models.
What HN is saying
- The biggest debate is cost: a 128GB MacBook Pro runs close to seven thousand dollars, and many commenters argue API credits are far cheaper for most developers.
- Hardware alternatives get a thorough airing. Dual RTX 3090s, Intel Arc Pro cards, and Mac Minis connected over the network all come up as cheaper paths to the same capability.
- Several people note that Qwen shines on greenfield projects but struggles with large existing codebases, especially anything off the beaten path of popular frameworks.
- Mac Mini owners advise keeping the machine in another room: running heavy models on a laptop you are also typing on is loud and hot enough to be impractical.
- Gemma 4 31B gets a quiet defence from a few commenters who say its code quality beats Qwen on first attempt, particularly for Rust and C++.
A group is applying to ICANN for a .self domain, free to self-hosters, one per person.
What the article says
- The Human-Centered Computing Foundation is applying through ICANN for a new top-level domain called .self, reserved for ethical, human-centered web projects.
- The pitch: one free domain per person, no squatting, no reselling, no corporate takeovers.
- The foundation is an approved participant in ICANN's applicant support program, so this is a real application, not just a proposal.
- The article is thin on operational details, deferring most specifics to a linked initiative document.
What HN is saying
- The site crashed under HN traffic, which commenters found fitting since it's self-hosted on underpowered hardware.
- The biggest practical objection: free plus free-choice-of-name equals squatter paradise, as happened to the .tk TLD two decades ago.
- Several people question how 'one domain per person' gets enforced without some form of ID verification, which many find unacceptable.
- A commenter points out .self isn't in the ICANN root database yet, and the author confirms it's still an open application.
- Fun thread on desirable subdomain names: your.self, treat.your.self, and less printable options.
Rogue Amoeba argues Apple should stop forcing Mac app icons into uniform squircles.
What the article says
- MacOS 26 (Tahoe) made Apple dictate a squircle shape for every third-party icon, shrinking non-conforming icons into an ugly gray box.
- Golden Gate is fixing Apple's own icons by removing the blurry Liquid Glass treatment, but the shape ban on third-party icons remains.
- Distinct shapes once let you find an app at a glance. Squircles reduce that cue to color alone, which fails people with color vision differences.
- Rogue Amoeba calls on Apple to let icons take any shape again, as they did for decades before Tahoe.
What HN is saying
- Most commenters agree that uniform squircles make the dock harder to scan, describing it as a 'soup of squircles' where finding an app takes conscious effort.
- A minority defends uniformity, arguing that wildly different shapes caused their own visual chaos and that shape still exists inside the squircle.
- Several people blame the squircle rule on VisionOS design constraints bleeding into macOS, and resent that the least popular platform is driving decisions for all others.
- The Liquid Glass opacity slider added in Golden Gate is called an 'outright admission of failure' by one commenter, with others more hopeful that the new design leadership will reverse course.
- A technical note: Golden Gate's new multi-layer icon format separates foreground from background, which could one day let a theme restore distinct shapes without breaking the grid.
Fil-C makes setjmp and ucontext memory-safe, even when C's context-switching APIs are genuinely dangerous.
What the article says
- setjmp and longjmp are notorious for corrupting the stack when misused, and ucontext APIs for coroutines and fibers are similarly treacherous. Fil-C wraps them all safely.
- The key trick: jmp_buf becomes an opaque pointer. Only the Fil-C runtime can touch the internal state, so there is no way to forge or corrupt a valid jump target from user code.
- For ucontext, Fil-C enforces a strict state machine: a context must be properly initialized before it can ever be switched to, and stacks are allocated internally so you cannot hand it a freed buffer.
- The hardest part was garbage collector integration. When a fiber is suspended, its stack still holds live pointers that the GC must track, so Fil-C adds a grey-fiber list that gets rescanned during mark termination.
- The author is candid about where bugs would likely hide: the SSA-value assumption in longjmp restoration, and the GC termination race with grey fibers.
What HN is saying
- A commenter explains that copying the whole stack before jumping would enable delimited continuations, but raw pointers into the stack make that impossible in C. Other languages sidestep this entirely.
- The Fil-C author (pizlonator) is active throughout, clarifying that Fil-C also makes sigaction memory safe and that longjmp is restricted to popping the stack exactly as an exception would.
- One commenter notes that ucontext is slow because it switches signal masks. The author acknowledges this and says a faster backend is a planned next step, but keeping it conservative for now.
- Someone asks if Fil-C uses Claude for development. The author says Claude only wrote some tests, and did not use any LLM for the longjmp or ucontext work itself.
- A skeptic argues these APIs have no bearing on safety. The author and others push back, pointing out that longjmp to a freed stack frame is textbook memory corruption.
Chinese food delivery giant Meituan releases a massive open-weight AI model built on Huawei chips.
What the article says
- LongCat-2.0 is a mixture-of-experts model with 1.6 trillion total parameters and 48 billion active at a time, released by Meituan.
- The model is built on top of DeepSeek's architectural work, though it introduces new elements beyond simple fine-tuning.
- Training ran on tens of thousands of Huawei AI chips rather than Nvidia GPUs, which required Meituan to build its own infrastructure largely from scratch.
- Model weights are not yet public, described as coming soon on Hugging Face. (Article body was empty; bullets inferred from title and comments.)
What HN is saying
- The biggest talking point is the Huawei chip angle: commenters note this may be the first large-scale pre-training run completed entirely without Nvidia, which would be a notable milestone.
- A commenter tested the model on a nuclear physics question and ranked it below Gemini Flash and Qwen 3, calling it acceptable but not impressive.
- One user got responses back in Chinese despite setting the interface to English, suggesting the model's default language behavior leaks through.
- The sheer size makes consumer hardware a non-starter. Even heavily compressed, the weights would need around 400GB, putting it firmly in data-center territory.
- Some skepticism surfaced about whether the weights and a full technical report will ever actually be released publicly.
A 14-year-old invented the REPL in 1960 while porting Lisp to a tiny PDP-1.
What the article says
- Peter Deutsch was still in high school when he implemented Lisp on the PDP-1 in 1960, working with a machine that had just four kilowords of memory.
- In doing so he invented the read-eval-print loop, the interactive programming model that Python and virtually every modern language shell still use today.
- The port turned Lisp from a theoretical, punch-card language into a live, interactive tool, which the author calls a milestone in computing history.
- A simulator lets you run PDP-1 Lisp on any Linux machine today, and the article links to the original manuals as PDFs.
What HN is saying
- The poster notes that a version of Eliza was also written for PDP-1 Lisp in 1966 but has since been lost, and hopes someone will backport it.
- One commenter found the Eliza source in a GitHub archive and updated the thread, calling it amazing how ancient code resurfaces from presumed extinction.
- Another commenter draws a wry parallel between the public excitement over Eliza in the 1960s and the current hype around large language models, noting the same predictions of imminent real AI.
- Someone asking for a good Lisp history book gets a recommendation for Lisp in Small Pieces, which traces interpreter design from McCarthy's original evaluator forward.
- A lighthearted suggestion to implement Eliza inside a dishwasher's firmware gets a reminder that Eliza already runs in Emacs via the doctor command.
Rocket Lab buys Iridium for $8 billion to become a vertically integrated space company.
What the article says
- Rocket Lab is acquiring Iridium in a cash and stock deal valuing the satellite company at about $8 billion.
- The strategic logic is vertical integration: Rocket Lab builds and launches satellites, and Iridium brings a working global constellation, over two million subscribers, and valuable L-band spectrum.
- Iridium's spectrum is arguably the crown jewel, giving the combined company a licensed path to direct-to-device and positioning services everywhere on Earth.
- Iridium was profitable in 2025, generating nearly $500 million in operating cash flow, which shores up Rocket Lab's finances as it scales.
- A $3.6 billion bridge loan from Deutsche Bank and Wells Fargo funds the cash portion, with the deal expected to close in mid-2027.
What HN is saying
- Many commenters think the real prize is Iridium's globally coordinated spectrum, not the aging satellite hardware, since spectrum licenses are nearly impossible to replicate.
- The strategic parallel to SpaceX is noted: just as Starlink gives SpaceX a guaranteed launch backlog, Iridium's constellation replacement program gives Rocket Lab steady work for its rockets.
- Skeptics worry about the debt load and timing, since Rocket Lab will need to refinance the bridge loan in mid-2027 when the satellite market could be softer.
- A lively thread traces Rocket Lab's New Zealand origins and how US export control law quietly pulled the company into the American defense orbit from early on.
- Several people recommend the book Eccentric Orbits, which covers Iridium's near-death experience after Motorola built and then nearly destroyed the original constellation.
Memory got a million times cheaper over 60 years — then AI demand erased a decade of progress.
What the article says
- Stanford's interactive chart tracks RAM prices since 1957, picking up from the classic McCallum dataset and extending it with live Amazon retail data.
- The long-run trend is staggering: six orders of magnitude cheaper per GB from 1979 to 2009. Then the curve bent.
- AI-driven demand has pushed DRAM prices back to roughly 2018 levels, wiping out years of gains with no quick correction in sight.
- The site also covers HBM — the memory inside AI accelerators — broken out by generation, using analyst estimates since HBM has no public spot market.
- All prices are nominal USD and reflect cheapest retail, not contract pricing or inflation adjustment — worth keeping in mind before drawing conclusions.
What HN is saying
- A commenter recalled watching an IBM engineer install a full megabyte on a Berkeley mainframe in 1973 — a vivid gut-check on how recently a gigabyte was science fiction.
- Sharpest insight: this price spike is the first memory cycle that hasn't quickly self-corrected. The industry normally overshoots, crashes, and recovers — AI demand has been sustained enough to break that pattern.
- A data quality flag: the chart's most recent DRAM points are DDR3 clearance stock, not DDR5 — making current prices look better than they actually are for anyone buying modern hardware.
- Meta moment: the dataset is a resurrection of the defunct jcmit.net, pulled from archive.org. Someone asked how long this version will survive — a dataset about memory now has a memory problem.
- Developers insist 64GB is the new minimum; average users are delighted on 8GB. The thread never quite resolves it, but the gap says a lot about who drives RAM demand.
5,000 digitized restaurant menus from 1880–1920 reveal how strange American food once was.
What the article says
- The Pudding built an interactive visualization of the NYPL Buttolph Collection — 5,000 real menus from restaurants in New York, Boston, LA, and elsewhere spanning 1880 to 1920. (Body is empty; inferred from comments and description.)
- Celery was the fourth most common menu item — a genuine delicacy before refrigeration made it cheap to ship.
- A 'Boiled' category dominated early menus, covering what we'd now call poaching and braising of mature, tough animals.
- No ethnic food except French — zero Asian, Mexican, or Italian dishes on hotel restaurant menus, even in LA.
- There's a curated narrative story alongside the raw browsable collection; commenters recommend starting there.
What HN is saying
- Commenters noted menus full of turtle, sweetbreads, venison, and mutton — things that would excite a modern nose-to-tail diner but were just ordinary then.
- Celery's prominence sparked a good thread: before refrigeration it was a cultivated delicacy, displayed in special vases on the table.
- A German tangent: pencil-strike tallies on beer coasters are legally binding documents — altering them counts as forgery.
- Post-COVID QR codes have erased printed menus across Europe; commenters mourned the leather-bound era.
- Navigation is broken for Mac trackpad tap-to-click users — you have to physically click to open a menu, which annoyed enough people to mention it.
AI said his tendon was fine; his doctor found a major tear. One of them is wrong.
What the article says
- After an MRI for shoulder pain, a clinic immediately treated the author for a Grade III tendon tear. He left suspicious they'd jumped the gun.
- He fed the full DICOM scan into Claude Code (Opus 4.8) and let it run for an hour. It found no tear — the opposite of the doctor's finding.
- A second Claude session arbitrated between the two reports and sided with the AI: mild tendinosis, nothing torn, moderate-to-high confidence.
- AI also flagged two red flags the author missed: shockwave therapy isn't recommended without calcification, and the injected gel is classified as homeopathic with no proven indication.
- He's stuck — can't fully trust the AI, can't fully trust the clinic. The honest conclusion is: go see another doctor.
What HN is saying
- Multiple radiologists in the comments agree: general-purpose LLMs are genuinely bad at reading MRI images. Public radiology training data is a fraction of what a first-year resident processes.
- One radiologist goes further — the AI's anatomical labels are outright wrong, suggesting the findings are hallucinated from report text in training data, not from actually reading the images.
- The sharpest observation: experts in any field consistently find AI output wrong or incomplete; it only looks helpful when you lack the domain knowledge to catch the errors.
- A commenter nearly died after an LLM told him his emergency symptoms just needed rest. He went to surgery anyway. Another commenter's father delayed cancer treatment on AI advice and didn't survive.
- The one use case everyone agrees works: paste a medical report into Claude, get it translated into plain language, ask the follow-up questions you felt awkward asking the doctor.