AI

Show HN: Viveka: filter LLM output against a Lean-verified Advaita Vedanta model (github.com) AI

Show HN introduces Viveka, a “witness-centered filter layer” for LLM apps that sits between a model and its user, extracts claims/postures from responses, and checks them against Lean-verified axioms from Scherf Logic’s formal Advaita Vedanta to flag or block objectifying, steering, or overconfident framing. The project is offered as the PyPI package “witness-layer,” with a strongly recommended [scherf] extra for the verified backend and options to FLAG/CORRECT/BLOCK rather than silently rewriting output, emphasizing a clear “honesty boundary” where claim interpretation is heuristic while the underlying axiom checks are machine-verified.

AI Doesn't Have ROI (wheresyoured.at) AI

The piece argues that “AI doesn’t have ROI” because the real costs of using LLMs are difficult to measure or even standardize, and that vendors have often obscured token costs while users spend more than they realize—an issue that has surfaced as services shift toward token-based billing and companies try to cut AI spend.

The Rise of Anti-AI AI Slop (theatlantic.com) AI

The Atlantic reports that protests against new AI data centers—supported in part by real concerns—have been accompanied online by “anti-AI AI slop,” including AI-generated rumors, images, and memes that target specific states and can mislead residents, potentially driven more by social-media engagement and monetization than genuine advocacy.

Martin Scorsese Is Embracing A.I (nytimes.com) AI

The New York Times reports that filmmaker Martin Scorsese is engaging with artificial intelligence as part of how the technology may be used in film and media production.

Expanding Project Glasswing (anthropic.com) AI

Anthropic says it is expanding Project Glasswing, which lets vetted partners use Claude Mythos Preview to scan codebases for vulnerabilities, from about 50 initial partners to roughly 150 new organizations across more than 15 countries. The company says partners have already found over 10,000 high- or critical-severity flaws and that the program will increasingly emphasize disclosing, fixing, and deploying patches, alongside tools like Claude Security. Anthropic also outlines plans to further broaden access and its Cyber Verification Program as advanced cyber-capability AI becomes more widely available.

Artificial Intelligence for Software Engineering: From Probable to Provable (cacm.acm.org) AI

The article argues that artificial intelligence methods for software engineering should progress from probabilistic approaches toward more rigorous, provable techniques, based on its “from probable to provable” framing.

Michael Burry says neither SpaceX nor Anthropic is worth $1T (businessinsider.com) AI

“Big Short” investor Michael Burry said he doubts SpaceX and Anthropic are worth $1 trillion, arguing there’s nothing in SpaceX’s IPO filing to justify that valuation and questioning whether Anthropic’s $965 billion fundraising price will ever be warranted given the high cost of building advanced AI models.

Tiiny AI Pocket Lab: The Offline Pocket‑Sized Supercomputer Revolution (wangdoo.com) AI

Wangdoo.com reports on the Tiiny AI Pocket Lab, a 300-gram USB‑C device that claims it can run large language models up to 120B parameters fully offline on a laptop using a dedicated CPU/NPU plus software optimizations (TurboSparse and PowerInfer), with no cloud, subscription, or token fees. The article says the device raised $1 million in five hours on Kickstarter and later topped $3 million, and it cites specific hardware and performance claims while also noting community concerns about the software stack and team transparency.

Taking the Training Wheels Off: Aligning LLMs Without Personas (lesswrong.com) AI

The article argues that current LLM alignment methods (like RLHF, steering vectors, and prompting) rely on “personas” or examples of good behavior found in training data, which may not generalize to superhuman models; it proposes “personaless alignment” as a research direction to achieve good behavior without copying specific moral exemplars.

Version 2.0 of AI laser mosquito defense system is here (twitter.com) AI

A post from Steven Cheng says a “Version 2.0” of an AI-driven laser mosquito defense system has been released, but no additional details were available from the provided content.

How to Build a Shitty Robot (mariozechner.at) AI

A maker describes turning a €10 low-cost toy robot into an LLM-powered, phone-controlled STEM project by disassembling it, hijacking its single-motor PCB via an Adafruit FT232H, and rebuilding the body from cardboard. The build uses a TypeScript client-server setup where the phone streams audio and renders tool actions while a laptop server handles speech-to-text, an LLM/agent, text-to-speech, and simple memory, with a voice pipeline designed for interactive “barge in” responses.

Mellum2 Goes Open Source: A Fast Model for AI Workflows (blog.jetbrains.com) AI

JetBrains says it has open-sourced Mellum2, a 12B parameter “focal” language model designed for faster, lower-cost production AI—using a Mixture-of-Experts approach where only part of the model is active per token. The post highlights use cases like routing/orchestration, low-latency RAG summarization, and running private or self-hosted AI workflows, and notes the model is specialized for natural language and code (not multimodal).

Angry devs vow to flee GitHub Copilot as metered billing takes hold (theregister.com) AI

The Register reports that GitHub Copilot users are threatening to leave after Microsoft introduced usage-based “metered” billing, with some developers claiming they burned through monthly AI credits in hours and saw much higher, hard-to-predict costs. Microsoft says the change reflects Copilot’s more compute-intensive agentic workflows and includes spending limits and usage dashboards, while commenters on forums and Reddit say they plan to switch to alternatives or build workarounds.

U.S. Midterms Have a Cyber Problem, but It's Not at the Ballot Box (blog.checkpoint.com) AI

Check Point says the biggest risk to U.S. midterm election integrity is not direct ballot or voting-machine tampering, but AI-enabled disinformation and “trust infrastructure” attacks such as phishing, brand impersonation, domain abuse, and credential theft aimed at election-adjacent targets.

AI costs how much? GitHub Copilot users react to new usage-based pricing system (arstechnica.com) AI

Ars Technica reports that after GitHub moved Copilot from request-based billing to a usage-based “AI credits” system in June 2026, many users are seeing steep cost shocks—some say they can burn through a month’s quota in a day—because credit usage depends on input/output token counts and the selected model.

Colorado Rolls Back Landmark AI Governance Law (bankinfosecurity.com) AI

Colorado has scaled back its landmark 2024 AI governance law by narrowing its scope and delaying enforcement, pushing implementation to Jan. 1, 2027 while removing several governance and compliance obligations criticized by industry groups. The changes follow Gov. Jared Polis signing a bill that revises the original framework for “high-risk” AI used across employment, lending, healthcare, insurance, housing, and education.

DeepMind CEO says those cutting jobs because of AI 'lack imagination' (inc.com) AI

DeepMind CEO Demis Hassabis criticized companies’ AI-driven job cuts, arguing they are misguided and reflect a “lack imagination,” according to the article referenced by Inc.

Crystal Nights by Greg Egan (gregegan.net) AI

“Crystal Nights” (by Greg Egan) follows Daniel, who unveils a single-processor photonic crystal AI platform and recruits Julie, a top AI researcher, to help “evolve” conscious, human-level artificial intelligence. Their negotiations quickly turn into an ethical debate over intervening in minds via evolution-like selection, including concerns about suffering and moral responsibility, as Daniel argues the alternative could be worse than his planned, carefully managed approach.

What's gonna happen to software engineers? (yakko.dev) AI

The post argues there’s no single answer to what will happen to software developers, but AI will likely change roles over a 5–10 year period, pushing many “software as a means to an end” developers to adapt rather than disappear. It frames devs into two broad motivations (“means to an end” vs “software as the end”) and outlines possible futures: business-as-usual with faster tooling, a shift toward “product builders” using natural language, or a move toward more user-facing product work where developers act more like PMs managing agent-driven teams.

Constraining LLMs Just Like Users (aeracode.org) AI

The post argues that LLM outputs should be treated like untrusted inputs and therefore controlled using techniques similar to human interface constraints—especially by constraining allowed output formats (e.g., fixed choices, JSON schemas, or grammars) and validating tool-call behavior to prevent overreach, while emphasizing transparency, user feedback, and limiting LLM tool access to what the user is allowed to do.

Can the stockmarket swallow Anthropic, SpaceX and OpenAI? (economist.com) AI

The article questions whether the stock market can absorb and value major private AI and space companies such as Anthropic, SpaceX, and OpenAI, based on the challenges and implications of bringing them into public markets.

After Automation: AI progress creates more work for humans, not less (every.to) AI

Every CEO Dan Shipper argues that recent AI progress is creating more (and different) work for humans rather than eliminating it, describing a shift toward human–AI collaboration where agents automate routine layers but still require human oversight for complex decisions and quality control.

The Frame Problem (plato.stanford.edu) AI

The article explains the “frame problem,” originally a technical challenge in logic-based AI of representing action effects without explicitly listing a vast number of non-effects, and then discusses the broader philosophical “epistemological” and related computational issues about how agents update beliefs while considering only what is relevant after an action.

Alphabet plans to raise $80B for AI goals (reuters.com) AI

Reuters reports that Alphabet is planning to raise up to $80 billion in equity capital to fund its AI spending goals.

AI's reality check has arrived (fastcompany.com) AI

The article, titled “AI's reality check has arrived,” suggests that AI is facing practical limitations or accountability pressures, though no article text was available to confirm specific details.