Hacker News

Over the past six months, there hasn’t been a single day where I’ve checked the HN Best RSS feed without seeing a post about how AI “writes bad code,” “introduces bugs,” “creates technical debt,” or something along those lines.

I’ll probably make a lot of enemies by saying this, but do people realize that code is just a means to an end?

Users don’t care whether the code was written by AI or by hand, or which framework you used. They care that the product works.

I say this as someone who has spent more than 20 years honing their craft as a software engineer.

Let’s face it: by the time I manually ship version 1.0 of a product, the AI-assisted version could have been deployed 10x faster. By then, enough real-world feedback would have surfaced to identify the major issues, and tools like Claude Code would make it possible to fix and ship version 2.0 at an incredible pace.

At some point, execution speed starts to matter more than the elegance of the code.

I've considered adding a search function, but I also kind of like that it requires a bit of exploration in the current form.

Some of the code is a bit clunky and I wouldn't mind refactoring it. I'm also not sure about browser compatibility - I've only got access to a couple of devices to test it on.

https://xcancel.com/eastdakota/status/2062860530360959273

More of these (via https://news.ycombinator.com/item?id=48418641 below):

https://xcancel.com/gregisenberg/status/2061794787825479818 (<-- the OP)

https://xcancel.com/dunkhippo33/status/2062768969560510486

https://xcancel.com/typesfast/status/2062791307094048937

https://xcancel.com/awxjack/status/2062605286683336757

https://xcancel.com/travisk/status/2062224472426365045

https://xcancel.com/mark_cummins/status/2062293061426663612

This paper is being published at ICLR 2026 (top AI conference), and was selected as one of three outstanding papers.

https://gds.blog.gov.uk/2026/06/02/building-for-the-future-m...

https://www.adyen.com/press-and-media/adyen-payments-gov-uk

https://general-instinct.com/).

After years of working in robotics, we kept running into the same problem: the best models never fit the hardware we actually had available.

The models that performed best were usually designed around datacenter assumptions: large GPUs, lots of memory bandwidth, and reliable network access. But most physical systems have the opposite constraints.

That led us down the path of figuring out how much of a frontier model could be preserved while still making it practical to run on edge hardware.

As part of that work, we recently open sourced InstinctRazor (https://github.com/General-Instinct/InstinctRazor)

One result we're excited about is compressing Qwen3.5-122B-A10B, a roughly 245 GB BF16 MoE model, into a 48 GiB GGUF. The resulting model is actually smaller than Gemma-4-26B-A4B while outperforming it on benchmarks like MMLU-Pro and GPQA-D etc. we preserve the parts that are always active (router, norms, Gated-DeltaNet/SSM layers, vision pathway, etc.) and quantize the routed experts much more aggressively. We then use on-policy distillation to recover capability lost during quantization.

The model can also run in a "small GPU" configuration where experts are streamed from system RAM. With an 8k context window, peak VRAM usage is around 7.6–8 GB.

If you're interested in the technical details, we wrote up the approach here (https://general-instinct.com/blog/frontier-moe-sub-4-bit)

We're especially interested in hearing from people deploying models onto robots or other edge devices. What models are you trying to run locally today? What has been the biggest bottleneck in getting them into production?

https://archive.ph/ZDxIe

https://lsc-pagepro.mydigitalpublication.com/publication/?i=...

PDF: https://cdn.coverstand.com/61061/865273/2c88ea662e2b57478723... (article is on page 62)

Related: https://www.404media.co/the-u-s-military-quietly-turned-gps-...

Then ChatGPT hit the scene and again, many of us dismissed it as a parlor trick that would never amount to much.

Using LLMs for coding initially was a only small step up from basic code completion, and a welcome farewell to Stack Overflow.

I am curious: what was the specific moment that you went from those quaint, dismissive observations to a slightly panicked, "Uh Oh" realization of what these models can do?

The experience of working with AI agents on this project changed a lot with recent model releases, as I describe in the readme. Opus 4.8 is able to provide algorithm implementation with formal proof in one shot, whereas previous models required me to provide proof strategies in multiple steps.

Trust in the correctness comes entirely from the Lean checker and human review of a small specification, not from the LLM.

Also check out the web demo built around the verified core linked in the readme: https://schildep.github.io/verified-polygon-intersection/. It supports multipolygons including holes, self intersections, and overlapping edges.