westurner

From "How did software get so reliable without proof? (1996) [pdf]" (2024) https://news.ycombinator.com/item?id=42425617 :

> From "The Future of TLA+ [pdf]" (2024) https://news.ycombinator.com/item?id=41385141 :

>> Formal methods including TLA+ also can't/don't prevent or can only workaround side channels in hardware and firmware that is not verified. But that's a different layer.

>> Things formal methods shouldn't be expected to find: Floating point arithmetic non-associativity, side-channels

Some heat pumps have built-in UV-C sanitization to prevent mold.

You can install an aftermarket UVC light to sanitize to prevent mold in heat pumps, humidifiers, etc

How to make this work with infrared wallpaper?

"Ask HN: Are there infrared wallpaper products in US markets?" (2025) https://news.ycombinator.com/item?id=45444514 :

> /? infrared wallpaper heating : https://www.google.com/search?q=infrared+wallpaper+heating

I'm aware of what evals are.

Do you think it is wise to optimize prompts for specific models or agents when there is a new model every month?

So, to build something like Co-Scientist the controls should be in the agent? Or RLHF'd like other things when training the model?

Is this also already true for diesel-electric locomotives? With regenerative braking?

Does that report include battery lifecycle costs and assume that sustainability score and hazards are equal?

Because of the topology and hydrology of the landform

Some notes on PQC and MTC roadmaps on "Shipping post-quantum cryptography to Python – The Trail of Bits Blog" https://news.ycombinator.com/item?id=48789958

mozilla/ssl-config-generator is now tlsref/configurar: https://github.com/tlsref/configurator .. http://configurator.tlsref.org/

So research methods like the scientific method are still subjective in AI implementation according to the AI experts?

Once there are - or next month when there will be - better models, agents, and agent harnesses for this, do you think that then we should concisely specify what is required instead of doing evals for particular models?

So meta-analysis and requisite language are too high-order for existing models and agents, and it's currently necessary to apply such procedural controls outside of the prompt?

What if there were a zero trust sandbox with costed opcodes? Then would there be alignment in AI systems as compared with non-AI systems?

Even with a perfect sandbox, an agent can slip in vulnerable code intentionally or accidentally. How different of a problem is that from hiring and HR is that? Idk. Perhaps easier to terminate a model.

"On the Impossible Safety of Large AI Models" (2022) https://arxiv.org/abs/2209.15259

"LLMs + Security = Trouble" (2026) https://arxiv.org/abs/2602.08422

> I think this false dichotomy between using LLMs and caring about quality/reliability needs to stop

I agree. What does coverage-guided fuzzing fuzz if there is 100% test coverage?

So, then, 100% branch test coverage is not a sufficient metric (because it doesn't indicate whether the code is fuzzed or formally verified for example).

Would Branch coverage even be a sufficient software quality metric if we were to instead measure how many times each branch of code is covered by tests? How to verify that one test which executes 100% of the code and runs only one assertion on, say, a CLI utility exit code integer is actually sufficiently covering?

> I think a codebase generated by AI is actually more understandable than one generated by humans at this point,

From doing a larger port (of sphinx, docutils, myst-md-parser, pygments, to rust in westurner/dsport) with a lot of human in the loop and currently ~80% branch coverage, this seems to be at least initially true but just like real life there's drift from even a good plan that you pay a more expensive model to prepare.

I suppose it's the same challenge as architectural drift in open source non-LLM-assisted products and the solutions are pretty much the same: give better instructions (AGENTS.md,) and use better sufficiency criteria as an engineering manager (branch test coverage, fuzzing, formal methods, TLA+), and train and pay humans to do secure code review.

Sometimes the agent doesn't notice that the code already solves for that and implements its own implementation with tests and it's wastefully redundant when the code should be refactored and the tests should be refactored so that we can delete code in order to minimize bloat.

Unfortunately often, just like IRL software development, the response from the agent is not sufficient to close the issue.

One proposed solution for this that is in retrospect obvious and also essential to success in "normal"/"traditional"/"legacy" (non-AI) engineering projects, is to always verify whether the candidate solution satisfies the criteria;

From "Groundtruth – checks your AI coding agent's claims against the Git diff" https://news.ycombinator.com/item?id=48838209 :

> "Follow up to verify that the work was actually satisfactorily completed"

> Are there other sound management practices that aren't yet effectively implemented in current gen agents?

Oh, and always write tests, docs, commit messages, and changelog entries; but don't waste tokens on documenting something that doesn't verifiably pass sufficient tests.

> I've never heard of grounding in this context, so it may help to describe what you want more clearly

An eval of this is likely worthwhile;

Re: "Grounded in logic" and "Grounded in theory"

Ground and justify all of the responses with logic and theory and real observations from qualified experiments with citations.

Present a coherent argument borne of logical premises with extant sufficient proven evidence of support. Assess and critique the response given such criteria that all responses should be valid logical arguments, and revise before responding

From https://linkeddiscoveries.ncbi.nlm.nih.gov/ :

> The initial phase of Linked Discoveries™ is an experimental pilot resource developed by the National Library of Medicine (NLM) that helps users explore how articles with PubMed® abstracts connect to the surrounding literature. Linked Discoveries creates neighborhoods of related articles, highlights shared conditions, genes, and chemicals across the neighborhood, and surfaces context like NIH funding, citation relationships, retractions, and reviews.

> Linked Discoveries uses data from PubMed, along with related condition, gene, and chemical data linked from other NLM databases like Gene, MedGen, and PubChem®.

So it does clique / principal components / flow analysis of the citation graph? Or GraphRAG and clustering?

OTOH, probably also useful: ChEMBL, UniProt, UpToDate, actual EHR charts, Folding@home, AlphaFold's db's, Co-Scientist that writes hypotheses, the LODcloud Linked Open Data Cloud

Re: cheminf: https://news.ycombinator.com/item?id=32894745

healthcare-ai GitHub topic: https://github.com/topics/healthcare-ai

Adaptation hypothesis: rotting fruit ferments, fermented water is cleaner, and overconsumption and liver failure lead to falling out of the tree.

Someday it'd be cool to generate spaced repetition flash card decks, apps, posters, and merch with: IPA symbols, Greek letters w/ LaTeX for math and science, English with non-regional diction; en, en_US, AAVE, sout3302, en_CA, en_GB,

Which dictionaries use IPA to spell out how to pronounce words?

Pronunciation respelling for English > International Phonetic Alphabet: https://en.wikipedia.org/wiki/Pronunciation_respelling_for_E... alphabet

open-dict-data/ipa-dict: Monolingual wordlists with pronunciation information in IPA; data/en_US.txt : https://github.com/open-dict-data/ipa-dict

Re: language learning: https://news.ycombinator.com/item?id=38317345 :

> [ Phonemic awareness, Phonological reasoning, ]

> What are some of the more evidence-based early literacy / reading curricula? OTOH: LETRS, Heggerty, PAL

Phoneme posters with IPA letters? Speech Pathology?

Human accessibility testing is important too.

Re: the new WebMCP spec: https://news.ycombinator.com/item?id=45623194 :

> Could this help with accessibility reviews?

> "Lighthouse accessibility score" https://developer.chrome.com/docs/lighthouse/accessibility/s...

> awesome-a11y > Tools: https://github.com/brunopulis/awesome-a11y/blob/main/topics/...

I've been working with Playwright e2e tests (End to End) in a number of projects lately.

Playwright is a useful and justified abstraction in the testing workflow because: Playwright tests can be run interactively in vscode (with just bwrap sandboxing but unfortunately no VM isolation), Playwright tests can be run in a dedicated local browser or in a headless browser for example in a container on GitHub Actions (a CI Continuous Integration system that runs then tests and saves the output artifacts automatically when any change is pushed to the source code repo), Playwright test can be recorded from an interactive browser session, and Playwright tests can be written by AI prompted to implement 100% branch test coverage with tests and e2e tests with {Playwright or Puppeteer} in {language}.

And then there's the Computer Use model (and the need for process or full VM sandboxing e.g. with ai-sandbox)

Find existing open source agent instructions and agent skills for UI/UX agents that could or should be adapted to a11y testing?

"The World as a Hologram" (1994) https://arxiv.org/html/hep-th/9409089v2 .. citations: https://scholar.google.com/scholar?cites=1288333106183032878...

But can there be holograms within the outer hologram?

Isn't there still need for non- or post- FIPS-140 -like cipher restrictions in non FIPS-140 environments?

How much code is needed to implement Classical+PQ (Hybrid PQ) or PQ-only (Only PQ) cipher selection restrictions just?

FWIU, with golang:

  # This allows X25519MLKEM768 (Hybrid PQ)
  GODEBUG=fips140=on

  # This prevents any PQ ciphers from being used:
  GODEBUG=fips140=only

tlsref needs to be revised to specify PQ cipher lists.

TIL MLIR supports sparse tensors with sparse_tensor.encoding = { dense, compressed, specialized structures for hypersparse regions, and hardware-specific sparse constraints, such as NVIDIA's 2:4 structured sparsity layout }

But it looks like [MLIR and all other implementations of] SIMD only accept vectors; so there can't be Zero-Copy there because the tensor must (?) be copied to a vector to pass to a SIMD e.g. matmul routine, and then the resultant vector must be copied back into a tensor only if there are subsequent references to the complete tensor instead of just a slice?

FWIU, AFAICS, GPUs are designed for 3x3 tensors (and affine transformation to 2D) but for greater degrees like for 4x4 tensors (e.g. for SQG) you must implement shaders?

Normed tensor Gaussian splatters are useful too.

From https://news.ycombinator.com/item?id=49855122 re: gravity from QED without any GR spacetime curvature, using the ~amplituhedron to solve n-body gravity with scattering amplitudes, and SQG/DDF:

> normed tensor gaussian splatters work well as a simulation primitive for this too because they do conservation.

Amplituhedron geometry simplifies scattering amplitude calculations.

N-body gravity derived from QED with scattering amplitudes (without spacetime curvature, in a Flat Minkowski space)

[...]

From this chat: https://share.google/aimode/O0pKsl6nYdv8Nh2fD :

> The "shear-jamming" of Fedi’s fluid space is the physical, hydrodynamical translation of hitting a geometric boundary facet of the amplituhedron

And, normed tensor gaussian splatters work well as a simulation primitive for this too because they do conservation.

Would those then be tensors?

Matrices are Tensors but with the matrix product operator instead of the tensor product operator.

Pandas supports MultiIndex DataFrames but the pandas docs recommend xarray for 3D and N-Dimensional data.

xarray supports N-Dimensional data as for example NetCDF but not tensor arithmetic.

xarray_jax: https://github.com/google-deepmind/xarray_jax :

> This library solves that problem. It registers xarray data structures as custom JAX PyTrees. This allows JAX to seamlessly flatten xarray objects into their raw arrays for accelerated computation and then unflatten the results back into fully labeled xarray objects, preserving critical metadata like dimension names and coordinates.

flatten and unflatten with datatypes is necessary for unrolling loops for performance.

Which is the correct logic for probabilistic logic, for expressions with frequentist or symbolic distributions as values? Are quantum logic and quantum statistical mechanics the appropriate or useful tools for all probabilistic logic?

dist_a1 <operator> dist_a2

uncertainties does mean±dev in Python with numpy types.

From https://news.ycombinator.com/item?id=41411280 :

> W3C CSVW supports per-column schema. ( with URIs for datatypes )

> Serialize a dict containing a value with uncertainties and/or Pint (or astropy.units) and complex values to JSON, then read it from JSON back to the same types. Handle datetimes, complex values, and categoricals

IEEE-754 specifies NaN (null), ±0, three infinities (positive, negative, and unsigned), but IEEE-754 does not specify a representation for categoricals, datetimes (like ISO8601), or complex numbers.

XSD (XML Schema Datatypes), which RDFS vocabularies often use to specify the rdfs:range of an rdfs:Property, does not specify how to specify abstract complex numbers; but OpenMath RDF, and QUDT (Quantities, Units, Dimensions, and Types) and OM Ontology all have a way to save complex numbers to disk, too.

virtio-gpu-rutabaga is another way to use a GPU from a KVM / Qemu guest which primarily Android Studio developed IIUC.

Notes re: how IOMMU GPU passthrough with device selection would be a helpful feature to add to QEMU cli, virt-manager,: https://news.ycombinator.com/item?id=46750715 :

> rutabaga_gfx does GPU paravirtualization: https://github.com/magma-gpu/rutabaga_gfx

Musk 2018 during a Trump administration: "Funding secured" [from SA]

> gold leaf on the walls of the people's house

Here's a picture of that: "Fact-checking Trump’s plaques for past presidents at the White House ‘Walk of Fame’ | PBS News" https://www.pbs.org/newshour/amp/politics/fact-checking-trum...

What regional interior decorating style is that, with the gold in those shapes on the wall?

Do they hypocritically disallow foreign ownership of media in their country?

"FCC Signs Off on Middle East Investments in Paramount-Warner Bros Deal" (2026) https://www.hollywoodreporter.com/business/business-news/mid...

Selling US media to foreign ownership?

Who else does that?

EA, FIFA, Madden, WB, CNN, Liv Golf, gold leaf on the walls of the people's house

What will be the new CNN?

The worst incentives.

Meshtastic InkHUD on a device w/ LoRA, BLE, Wifi; https://news.ycombinator.com/item?id=49060831 , an unfinished start at weather, and then Mu Animation Video w/ gaussian splatting to do better than GIF on ESP32-S3, and then also on TI-84: https://news.ycombinator.com/item?id=49262597 , story: https://news.ycombinator.com/item?id=49060309

From https://news.ycombinator.com/item?id=48775482 :

> Waveshare has a E6 full color ePaper/eInk/EPD display in 3.6" and 7.3"

A digital picture frame that's wall-powered could act as a node in a Meshtastic or MeshCore LoRA mesh.

From https://news.ycombinator.com/item?id=44693991 :

> EPS3.9 also had significant anti-tumor effects in the mice with liver cancer and activated anti-tumor immune responses

“A Novel Exopolysaccharide, Highly Prevalent in Marine Spongiibacter, Triggers Pyroptosis to Exhibit Potent Anticancer Effects” (2025) DOI: 10.1096/fj.202500412R https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.2025004...

"A Gemma model helped discover a new potential cancer therapy pathway" https://news.ycombinator.com/item?id=45604231 :

> eCPMV VNPs + EPS3.9 + [...]

"Scientists are discovering a powerful new way to prevent cancer" https://news.ycombinator.com/item?id=45474404

Notes re: Kidneys not Livers: https://news.ycombinator.com/item?id=47460486 ; gh/topic/healthcare-ai