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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
Four thoughts on Anthropic's Fable 5 kerfuffle
ProductMind · 2026-06-16 · via Hacker News - Newest: "LLM"

The reports point to Amazon being the source of the ‘tattling’ to the US Govt about potential malicious use of Fable 5. And then the US Govt asking for a specific unprecedented restriction on Mythos and Fable 5: “no access for any foreign national inside or outside the United States, including Anthropic’s own employees”. Swatting depends on a roided-out SWAT team to react peremptorily to a malicious phone call, sans careful investigation. And this is what it seems happened here - Amazon (Anthropic’s largest investor) sent a written report to the US govt, in which it posited dangerous use of Claude - something that needed careful evaluation on its merits.

If you’re experienced in technology and policy, you know the restriction requested from Anthropic can’t be administered at the level of national citizenship. So it’s all or nothing (Anthropic chose ‘all’, restricting it for everyone). We think it’s wrong to frame it as ‘Anthropic was recalcitrant’ as David Sacks did. Usually, the asker (the USG) should have the sophistication to understand the choices imposed by its request. No company can build the infrastructure to filter out non-US citizens’ access to globally deployed software platforms. Only the US government has done it at scale.

And then there is the matter of whether the ‘jailbreak’ was legitimate. Actual details are scant, even with Anthropic’s official statement and Axios’ reporting, and it comes down to trust: whether you trust the flawed but safety-minded AI company or the top tier of the Trump administration. The internet is unequivocal on that one.

This is an important moment in the history of the evolution of Artificial Intelligence. On the surface, LLMs have been driven by research labs for the last 5 years. Even though most of them are for-profit, it seems to have worked well; research papers flowed from Google, OpenAI, Anthropic, Meta, Microsoft, Qwen, DeepSeek, and more. But underneath what seems governed by commercial intent, nations have been jockeying and negotiating for what amounts to: ‘who gets to make the best AI and call the shots’. The US has advantages in energy density per capita, data-center build-out potential, capital flows, and intellectual property. It has explicitly blockaded the chip layer (temporary fix) but has not pulled the same for the model layer. Well, it did this week, in a shocking, ham-handed fashion. It cannot be overstated how important this moment is. The United States imperiously ordered that effectively EVERYONE in the world would not get a certain piece of software, which many depended on. No laws were passed, and very few deliberations were held. And it was publicly framed as adversarial to anyone not a United States citizen, even though the policy eventually caught up to citizens, too.

This reveals LLMs as geopolitical ordnance deployed by foreign powers in exclusionary ways against other nations and their citizens. I’m sure the Chinese are not surprised (they offer a different kind of AI diplomacy with their competent, famous, and cheap open-weight models). But the Europeans are likely very pained at this moment. Awkward.

We expect second and third-order consequences, including the following:

1. Public research on LLMs will continue to decline1.

2. Nations will hoard breakthroughs.

3. For-profit labs will yield more and more to the regulation of their nation-state cradles.

4. Some of the new ones will try to be stateless if they can manage it, and still secure venture or other funding.

It was imprudent for the US to tip its hand this early. Oh well. I think we will all regret it together.

In response to this model yank, we see a lot of speculation about local AI being the future of AI proliferation. The general story goes something like this: Moore’s law will make CPU chips do native inference on phones, laptops, and desktops up to a level beyond Mythos. In 5 to 10 years, everyone will have models running locally and problem-solved.

What this ignores is relative advantage and competition between nations and companies. Moore’s Law is actually a relentless upgrade cycle law. When new chips emerge, the most competitive productivity-minded companies HAVE to upgrade to keep up.

Does anyone remember IT departments handing out the beefiest laptops to developers every 2 years? Does anyone remember the big upgrade cycle driven by the emergence of the Apple M1 and M2 chips?

The best models will always run in the data center first, because the best and highest-performing chips always appear there first. It’s a lucrative and easy supply chain target, while phones, laptops, and PCs are incredibly slow to field upgrade.

Frontier models whose capabilities will outstrip your capable PC models will always come to you via API first, in a world economy built around relative performance. And in a world where AI supremacy is a relative acceleration race, local AI will be a second-tier productivity cushion, not the ceiling of performance. And if you think otherwise, you have not contemplated why Azure and AWS exist in the first place.

The future of LLMs is both local and datacenter. It’s not one or the other; it’s complementary. In the very same way, it’s already complementary today. To wit, most businesses use AWS and still issue powerful PCs to their employees. What is yet to emerge is the ‘cooperation layer’ that connects the two more seamlessly.

One more thing. This ham-handed episode is a prime example of an unforced error. The matter required more delicacy in handling than was exhibited by the Commerce Department and its secretary. This need not have become a domestic and international incident. It has all the hallmarks of an unsophisticated administrator trying to use insufficient evidence to boss a free-willed civilian business around. One that had tried its best to not only submit its IP to oversight but had put in guardrails to prevent circumvention2. It feels like a more consultative approach, looser deadlines, and more assistance would have finessed this last critical engagement. It’s hard not to chalk it up to the ineptness of the named players like Andy Jassy, Treasury Secretary Scott Bessent, and Commerce Secretary Howard Lutnick.

If it’s any comfort, you can be 100% sure Fable 5 will be back; after all, in a year, Mythos class models will be ordinary, even if neutered. But the world of AI will never be the same again.

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