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OpenAI Codex App: death of the VSCode fork, multitasking worktrees, Skills Automations
codex openai agent-based-systems parallel-processing software-testing developer-workflows automation product-feedback-loop neurosymbolic-ai benchmarking sama reach_vb gdb skirano embirico ajambrosino thsottiaux nbaschez yuchenj_uw badlogicgames random_walker
OpenAI launched the Codex app on macOS as a dedicated agent-native command center for coding, featuring multiple agents in parallel, built-in worktrees for conflict isolation, skills for reusable bundles, and scheduled automations. The app emphasizes developer workflows like Plan mode for upfront task decomposition and is gaining positive adoption signals from insiders including @sama. There is movement towards ecosystem standardization of skills folders, signaling early conventions in agent tooling. Codex also exemplifies a "self-improving" product feedback loop combining humans and agents. In coding agents practice, best practices include a "test-first" approach to bug fixes, the "conductor" model where one developer manages 5-10 agents in parallel, and a neurosymbolic framing explaining why coding agents succeed due to software's verifiability and symbolic tooling. Benchmark skepticism remains about productivity studies that do not reflect agentic workflows.
not much happened today
glm-4.7 claude-code z.ai meta-ai-fair manus replit agentic-architecture context-engineering application-layer code-generation agent-habitats ai-native-llm ipo inference-infrastructure programming-paradigms zixuanli_ jietang yuchenj_uw sainingxie amasad hidecloud imjaredz random_walker
Z.ai (GLM family) IPO in Hong Kong on Jan 8, 2026, aiming to raise $560M at HK$4.35B, marking it as the "first AI-native LLM company" public listing. The IPO highlights GLM-4.7 as a starting point. Meta AI acquired Manus for approximately $4–5B, with Manus achieving $100M ARR in 8–9 months, illustrating the value of application-layer differentiation over proprietary models. Manus focuses on agentic architecture, context engineering, and general primitives like code execution and browser control, emphasizing "agent habitats" as a competitive moat. Discussions around Claude Code highlight skepticism about "vibe coding," advocating for disciplined, framework-like AI-assisted programming practices.
not much happened today
phi-4 phi-4-mini-reasoning qwen3-235b qwen3-moe-235b qwen3-moe-30b qwen3-dense-32b qwen3-dense-14b qwen3-dense-8b qwen3-dense-4b qwen3-dense-0.6b qwen2.5-omni-3b deepseek-prover-v2 llama llama-guard-4 prompt-guard-2 mimo-7b microsoft anthropic cursor alibaba togethercompute deepseek meta-ai-fair xiaomi openrouterai cohere reasoning model-fine-tuning model-evaluation benchmarking model-popularity open-source math model-scaling model-filtering jailbreak-prevention cline reach_vb vipulved akhaliq omarsar0 zhs05232838 huajian_xin mervenoyann karpathy random_walker sarahookr blancheminerva clefourrier
Microsoft released Phi-reasoning 4, a finetuned 14B reasoning model slightly behind QwQ but limited by data transparency and token efficiency issues. Anthropic introduced remote MCP server support and a 45-minute Research mode in Claude. Cursor published a model popularity list. Alibaba launched Qwen3-235B and other Qwen3 variants, highlighting budget-friendly coding and reasoning capabilities, with availability on Together AI API. Microsoft also released Phi-4-Mini-Reasoning with benchmark performance on AIME 2025 and OmniMath. DeepSeek announced DeepSeek-Prover V2 with state-of-the-art math problem solving, scaling to 671B parameters. Meta AI's Llama models hit 1.2 billion downloads, with new Llama Guard 4 and Prompt Guard 2 for input/output filtering and jailbreak prevention. Xiaomi released the open-source reasoning model MiMo-7B trained on 25 trillion tokens. Discussions on AI model evaluation highlighted issues with the LMArena leaderboard, data access biases favoring proprietary models, and challenges in maintaining fair benchmarking, with suggestions for alternatives like OpenRouterAI rankings. "LMArena slop and biased" and "61.3% of all data going to proprietary model providers" were noted concerns.
not much happened today
gpt-4.5 gpt-4 gpt-4o o1 claude-3.5-sonnet claude-3.7 claude-3-opus deepseek-v3 grok-3 openai anthropic perplexity-ai deepseek scaling01 model-performance humor emotional-intelligence model-comparison pricing context-windows model-size user-experience andrej-karpathy jeremyphoward abacaj stevenheidel yuchenj_uw aravsrinivas dylan522p random_walker
GPT-4.5 sparked mixed reactions on Twitter, with @karpathy noting users preferred GPT-4 in a poll despite his personal favor for GPT-4.5's creativity and humor. Critics like @abacaj highlighted GPT-4.5's slowness and questioned its practical value and pricing compared to other models. Performance-wise, GPT-4.5 ranks above GPT-4o but below o1 and Claude 3.5 Sonnet, with Claude 3.7 outperforming it on many tasks yet GPT-4.5 praised for its humor and "vibes." Speculation about GPT-4.5's size suggests around 5 trillion parameters. Discussions also touched on pricing disparities, with Perplexity Deep Research at $20/month versus ChatGPT at $200/month. The emotional intelligence and humor of models like Claude 3.7 were also noted.