[AINews] Why OpenAI Should Build Slack

# [AINews] Why OpenAI Should Build Slack

[![](https://substackcdn.com/image/fetch/$s_!XQAE!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89ee056a-0ea2-4473-8e1c-9b21f034c717_1474x2116.png)](https://substackcdn.com/image/fetch/$s_!XQAE!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89ee056a-0ea2-4473-8e1c-9b21f034c717_1474x2116.png)

[![](https://substackcdn.com/image/fetch/$s_!tKgz!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fee585153-ea30-423a-a0ab-ee335df4fbc8_1528x1254.png)](https://substackcdn.com/image/fetch/$s_!tKgz!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fee585153-ea30-423a-a0ab-ee335df4fbc8_1528x1254.png)

[![](https://substackcdn.com/image/fetch/$s_!6DLA!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F96ecb132-a3fd-4116-976d-853805724dbd_1384x1344.png)](https://substackcdn.com/image/fetch/$s_!6DLA!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F96ecb132-a3fd-4116-976d-853805724dbd_1384x1344.png)

* **Is it hard to do?** yes for almost everyone except you
* **Is it a big deal if you get it right?** yes for us users, but an even bigger deal for your business
* **Will you have lots of low hanging fruit to build new agentic interfaces and a [context graph](https://www.latent.space/p/ainews-context-graphs-hype-or-actually)/system of record to power [Frontier](https://www.latent.space/p/ainews-ai-vs-saas-the-unreasonable) and everything else you do in SMB and Enterprise?** yeah.

# AI News — Feb 13, 2026

# **AI Twitter Recap**

* **MiniMax-M2.5 is now open source**: MiniMax released **MiniMax-M2.5** weights + code, positioning it as an “agent-native” model trained with **RL across hundreds of thousands of real-world environments** for coding, tool use, search, and office workflows ([MiniMax announcement](https://twitter.com/MiniMax_AI/status/2022310932693897628)). vLLM highlights day‑0 support and reports key benchmark numbers: **80.2% SWE‑Bench Verified**, **76.3% BrowseComp**, plus claims around training scale (200k+ RL environments) and speed/cost characteristics ([vLLM](https://twitter.com/vllm_project/status/2022311342225678757)). SGLang similarly ships day‑0 support and frames the model as production-grade for “always-on” agents ([lmsys](https://twitter.com/lmsysorg/status/2022319102560555401)).
* **The practical headline is economics + throughput, not just score**: MiniMax repeatedly markets **“$1 per hour at 100 tps”** (interpretable as a “long-horizon agent budget”), which shows up both in their own posts ([MiniMax](https://twitter.com/MiniMax_AI/status/2022379949336957254)) and in community recaps emphasizing that low activated-parameter count makes self-hosting plausible ([omarsar0](https://twitter.com/omarsar0/status/2022384166034190528)). Early local runs suggest unusually strong on-device viability for its class: MLX users report ~**50 tok/s** shortly after release ([pcuenq](https://twitter.com/pcuenq/status/2022336556326060341)), and a single **M3 Ultra 512GB** run at **6‑bit** reports ~**40 tok/s** with ~**186GB** peak memory ([ivanfioravanti](https://twitter.com/ivanfioravanti/status/2022338870172684655)).
* **Forge RL training system details leak into the narrative**: A Zhihu-derived writeup summarizes MiniMax’s “Forge” RL stack as still **CISPO-like**, using **process reward + completion-time reward**, with infrastructure tricks like **multi-level prefix cache** and high rollout compute share (claimed **~60%** of compute) generating **millions of trajectories/day** ([YouJiacheng](https://twitter.com/YouJiacheng/status/2022339475049947576)). MiniMax leadership explicitly answers parameterization tradeoffs (“**10B active** intentional”), claims proximity to “**infinite agent scaling**” with **knowledge capacity** as the limiter, and teases structural + pretraining innovation focus for **M3** ([MiniMax reply](https://twitter.com/MiniMax_AI/status/2022370086397624476)).
* **Independent reviews: “viable for multi-turn work” but token-hungry**: A Chinese review thread claims M2.5 corrects M2.1’s imbalance (coding up, logic down), with overall improvements and better stability; it notes **high token usage** (nearly **2× Sonnet** in one comparison) but frames pricing/compute as making it usable day-to-day ([ZhihuFrontier](https://twitter.com/ZhihuFrontier/status/2022214461415993817)). Another summary calls it “≤Sonnet for coding, but close,” and emphasizes multi-turn viability as the key break from “toy” open models ([teortaxesTex](https://twitter.com/teortaxesTex/status/2022223441005621556)).
* **Ecosystem uptake is immediate**: weights mirrored and packaged across tooling (Hugging Face release pings, GGUF/quant drops, etc.), including Intel-hosted quantized artifacts like a **2‑bit GGUF** for MiniMax‑M2 and **INT4** for Qwen3‑Coder‑Next ([HaihaoShen](https://twitter.com/HaihaoShen/status/2022293472796180676)).

* **GLM‑5 positioning**: Together markets GLM‑5 as best-in-class open-source for long-horizon agents and systems engineering, quoting metrics like **77.8% SWE‑Bench Verified**, **50.4% HLE w/ tools**, and a MoE efficiency story with “DeepSeek Sparse Attention” (as described in the tweet) ([Together](https://twitter.com/togethercompute/status/2022354579858289052)). W&B promotes an interview claiming **744B params**, a “new RL framework,” and “fully open source under MIT” (as stated in the post) ([W&B](https://twitter.com/wandb/status/2022389206572765697)). Community members also notice dataset fingerprints like “truthy‑dpo” appearing in GLM‑5 outputs ([jon\_durbin](https://twitter.com/jon_durbin/status/2022291772617945546)).
* **GLM‑5 qualitative review highlights**: A detailed Zhihu-based comparison frames GLM‑5 as a substantial improvement over GLM‑4.7, especially on hallucination control, programming fundamentals, and character processing—but also more verbose/token-expensive and prone to “overthinking,” suggesting a trade between long-horizon reasoning and compute burn ([ZhihuFrontier on GLM‑5](https://twitter.com/ZhihuFrontier/status/2022161058321047681)).
* **Benchmarks as a moving target**: There’s persistent meta-discussion about whether leaderboards/evals are saturated or misleading. Examples: concerns that tokens/latency tradeoffs hide true capability; skepticism about inferring model size from TPS; and the observation that past “SWE‑bench saturation” claims were premature ([jyangballin](https://twitter.com/jyangballin/status/2022367240293949772), [teortaxesTex](https://twitter.com/teortaxesTex/status/2022255213394948360)).
* **Cross-checking with alternative evals**: SWE‑rebench is cited as “brutal” for some recent releases and shows different relative rankings than SWE‑bench Verified; a caution is made to treat it as “additional signal” ([maximelabonne](https://twitter.com/maximelabonne/status/2022401174549512576)).

* **Claude Code “Agent Teams” internals are surprisingly simple**: A reverse-engineering summary claims Claude Code’s multi-agent comms use **JSON files on disk** (inboxes under `~/.claude/teams/inboxes/{agent}.json`), with polling between turns and JSON-in-JSON protocol messages; the argument is that this is a pragmatic CLI design (no Redis/queues) and improves observability at the cost of atomicity/backpressure ([peter6759](https://twitter.com/peter6759/status/2022156692985983266)).
* **Terminal agents are becoming the default UX**: Cline launches **Cline CLI 2.0**, an open-source terminal coding agent featuring a redesigned interactive TUI, parallel agents with isolated state, headless CI/CD mode, and broad editor support (ACP for Zed/Neovim/Emacs) ([cline](https://twitter.com/cline/status/2022341254965772367), [cline details](https://twitter.com/cline/status/2022341258979717196)). Community framing: “open-source strikes back” due to free/low-barrier access to strong models ([testingcatalog](https://twitter.com/testingcatalog/status/2022348951459172604), [dr\_cintas](https://twitter.com/dr_cintas/status/2022387444189139367)). One Cline team member describes a full rewrite (Go → TypeScript) driven by architecture/UX pain and the need to run evals reliably ([arafatkatze](https://twitter.com/arafatkatze/status/2022415192932651302)).
* **Agent scaffolds may matter less than expected (for some horizons)**: METR-related discussion suggests Claude Code / Codex scaffolds don’t strongly outperform METR’s “simple OS scaffolds” on measured time horizons so far ([nikolaj2030](https://twitter.com/nikolaj2030/status/2022398669337825737)), with Ajeya Cotra noting surprise at the small delta ([ajeya\_cotra](https://twitter.com/ajeya_cotra/status/2022419978495127828)). In contrast, others note that for longer, harder tasks, scaffold choice can matter materially (e.g., **~10% success** swings) ([gneubig](https://twitter.com/gneubig/status/2022451119310655909)).
* **“Agents as OS / filesystem as substrate”**: Several posts converge on the idea that file systems are the natural environment for agents (observability, unstructured data manipulation). Box announces integration as a “cloud filesystem” into LangChain deepagents ([levie](https://twitter.com/levie/status/2022375298097111160)). WebMCP pushes “browser is the API” for web automation without UI perception, with a DoorDash-like starter template ([skirano](https://twitter.com/skirano/status/2022387763421810989)).
* **Key operational lesson: make codebases “agent-ready”**: A crisp observation is that agents have “zero tolerance” for entropy humans route around; they treat dead code/outdated docs literally, forcing engineering hygiene that humans always needed but often deferred ([dok2001](https://twitter.com/dok2001/status/2022339274767520246)).

* **Length-Incentivized Exploration (LIE) for reasoning**: A research summary introduces the “Shallow Exploration Trap” (long reasoning trajectories become exponentially unlikely under AR sampling), and proposes LIE: a length reward + redundancy penalty to encourage broader in-context exploration without filler. Reported gains include **AIME25 20.5%→26.7%** in one setup and small but consistent improvements across other benchmarks/models ([dair\_ai](https://twitter.com/dair_ai/status/2022360649817526275)).
* **DPPO vs PPO and “trust region” framing**: A long algorithm breakdown contrasts PPO’s token-ratio clipping with DPPO’s distribution-shift control via divergence measures (TV/KL), plus approximations (binary/top‑K) to reduce compute, arguing DPPO is more proportional on rare tokens and better constrains large probability-mass moves ([TheTuringPost](https://twitter.com/TheTuringPost/status/2022326245745377562)).
* **Rubrics-as-rewards and evolving rubrics**: A thread describes **RLER** (RL with evolving rubrics) in Dr. Tulu: seed rubrics with search-grounded criteria, maintain an evolving rubric buffer per prompt, and keep the most discriminative rubrics by reward variance to combat reward hacking and adapt evaluation on-policy ([cwolferesearch](https://twitter.com/cwolferesearch/status/2022384365049892974)). Separately, a take argues “rubrics as rewards” can beat verifiers-as-reward even in formal-verification settings, recommending verifiers in the loop/harness but not as the sole reward signal ([davidad](https://twitter.com/davidad/status/2022361016995319850)).
* **∆Belief‑RL / information-seeking agents**: A new approach rewards actions by how much they increase belief in a target (logprob-based), aiming for long-horizon information seeking without a critic/reward model; claims include generalization from “20 questions” training to new tasks and continued improvement when scaling interaction time ([ShashwatGoel7](https://twitter.com/ShashwatGoel7/status/2022341054939185345)).
* **Human simulation as an RL target**: Stanford’s **HumanLM** + **Humanual** benchmark propose training LLMs to simulate user responses accurately (human-centric evaluation, preference shaping, policy justification), positioning user simulation as a capability primitive for product/agent design ([ShirleyYXWu](https://twitter.com/ShirleyYXWu/status/2022374624676421676)).

* **vLLM on GB300 + FP4 MoE acceleration**: vLLM reports DeepSeek R1 on **GB300** with **22.5K prefill TGS** and **3K decode TGS per GPU**, claiming large improvements over Hopper, and highlights a recipe including **NVFP4 weights** and **FlashInfer FP4 MoE kernel** (`VLLM_USE_FLASHINFER_MOE_FP4=1`) plus disaggregated prefill and tuning notes ([vllm\_project](https://twitter.com/vllm_project/status/2022308974150975792)).
* **DeepSpeed ZeRO load-time fix**: A rework moves tensor flattening from CPU to GPU, significantly improving multi-GPU load times for huge models under ZeRO 1+2 ([StasBekman](https://twitter.com/StasBekman/status/2022354880049082658)).
* **Gemini “Skills” and multimodal tool calling**: Google’s Gemini API work includes a “skills” repo teaser ([osanseviero](https://twitter.com/osanseviero/status/2022259577232785866)) and an Interactions API update enabling **multimodal function calling** where tools can return **images** and Gemini can process returned images natively ([philschmid](https://twitter.com/_philschmid/status/2022349886318928158)). AI Studio billing/upgrade UX is streamlined (upgrade to paid without leaving Studio, usage tracking, spend filters) ([OfficialLoganK](https://twitter.com/OfficialLoganK/status/2022409335465480346), [GoogleAIStudio](https://twitter.com/GoogleAIStudio/status/2022409735287537999)).
* **Agent harness instrumentation**: ArtificialAnalysis adds end-to-end speed tracking to their agent harness **Stirrup**, plus per-model breakdowns and tool-call latency metrics—explicitly treating wall-clock completion time as a first-class agent metric ([ArtificialAnlys](https://twitter.com/ArtificialAnlys/status/2022358995739254800)).
* **Local fine-tuning & Apple Silicon workflows**: Notable tooling for MLX: real-time transcription with Voxtral Mini 4B in MLX Swift ([awnihannun](https://twitter.com/awnihannun/status/2022322714548338962)), a no-code local fine-tuning tool exporting to Ollama ([awnihannun](https://twitter.com/awnihannun/status/2022327214218657948)), and a repo of MLX-LM LoRA examples including GRPO/ORPO/DPO variants ([ActuallyIsaak](https://twitter.com/ActuallyIsaak/status/2022414004623479014)).

* **OpenAI claims a novel theoretical physics result with GPT‑5.2**: OpenAI announces a preprint showing a gluon interaction previously assumed not to occur can arise under a specific “half-collinear” regime, framed as AI-assisted discovery ([OpenAI](https://twitter.com/OpenAI/status/2022390096625078389); preprint link is shared in-thread: [arXiv pointer](https://twitter.com/OpenAI/status/2022390104237707667)). Kevin Weil adds color: GPT‑5.2 Pro suggested a general formula; an internal scaffolded model then **proved it after ~12 hours** of continuous work ([kevinweil](https://twitter.com/kevinweil/status/2022388305434939693)). Discussion emphasizes that pattern-finding + sustained scaffolded reasoning is the differentiator, not just a single chat completion.
* **Community reactions range from “significant journal-paper tier” to skepticism about interpretation**: Some report physicists calling it a meaningful contribution roughly equivalent to a solid journal paper ([polynoamial](https://twitter.com/polynoamial/status/2022413904757035167)); others focus on the implications of long-duration productive reasoning and how to measure it in tokens/time ([teortaxesTex](https://twitter.com/teortaxesTex/status/2022401945429000685)). There’s also meta-discussion about how many employees (or outsiders) can actually evaluate the proof/result, underscoring the evaluation gap for domain-elite work ([scaling01](https://twitter.com/scaling01/status/2022401147110318586)).

* **GitHub adds ability to disable PRs** ([joshmanders](https://twitter.com/joshmanders/status/2022170444116414790), [jaredpalmer](https://twitter.com/jaredpalmer/status/2022395520623480970)).
* **OpenAI’s GPT‑5.2 physics announcement** ([OpenAI](https://twitter.com/OpenAI/status/2022390096625078389)).
* **MiniMax M2.5 open-source release** ([MiniMax](https://twitter.com/MiniMax_AI/status/2022310932693897628)).
* **Cline CLI 2.0 launch / open-source terminal agent** ([cline](https://twitter.com/cline/status/2022341254965772367), [testingcatalog](https://twitter.com/testingcatalog/status/2022348951459172604)).
* **“I am the bottleneck now” (agent-era productivity reflection)** ([thorstenball](https://twitter.com/thorstenball/status/2022310010391302259)).
* **Humanoid robotics hands progress (Figure)** ([adcock\_brett](https://twitter.com/adcock_brett/status/2022353637964751221)).

# **AI Reddit Recap**

* **[MiniMaxAI/MiniMax-M2.5 · Hugging Face](https://www.reddit.com/r/LocalLLaMA/comments/1r3pxy7/minimaxaiminimaxm25_hugging_face/)** (Activity: 531): **MiniMaxAI has released the MiniMax-M2.5 model on [Hugging Face](https://huggingface.co/models?sort=modified&search=minimax+m2.5), which is noted for its advanced performance in coding, tool use, and office tasks. The model maintains a size of** `220 billion` **parameters, contrary to expectations of an increase to** `800 billion` **like the GLM5 model. It offers a cost-effective operation at** `$1 per hour` **for** `100 tokens per second`**, and is enhanced by the Forge reinforcement learning framework, which improves training efficiency and task generalization.** Commenters express surprise at the model’s size remaining at `220 billion` parameters, highlighting its impressive performance despite not increasing in size. There is also anticipation for the release of a **GGUF** quantization format, which is not yet available.

* **[MiniMaxAI MiniMax-M2.5 has 230b parameters and 10b active parameters](https://www.reddit.com/r/LocalLLaMA/comments/1r35d2x/minimaxai_minimaxm25_has_230b_parameters_and_10b/)** (Activity: 523): **OpenHands has announced the release of the MiniMaxAI MiniMax-M2.5 model, which features** `230 billion` **parameters with** `10 billion` **active parameters. This model is noted for its performance, ranking 4th in the OpenHands Index, and is** `13x` **more cost-effective than Claude Opus. It excels in long-running tasks and issue resolution but requires improvements in generalization and task execution accuracy. The model is available for free on the OpenHands Cloud for a limited time. [Source](https://huggingface.co/cerebras)** Commenters are optimistic about the potential of a `~160B` REAP/REAM hybrid version, which could be optimized for machines with `128GB` of RAM, suggesting a focus on quantization and performance efficiency.

* **[Minimax M2.5 Officially Out](https://www.reddit.com/r/LocalLLaMA/comments/1r2xotu/minimax_m25_officially_out/)** (Activity: 765): **Minimax M2.5 has been officially released, showcasing impressive benchmark results:** `SWE-Bench Verified` **at** `80.2%`**,** `Multi-SWE-Bench` **at** `51.3%`**, and** `BrowseComp` **at** `76.3%`**. The model is noted for its cost efficiency, with operational costs significantly lower than competitors like Opus, Gemini 3 Pro, and GPT-5. Specifically, running M2.5 at** `100 tokens per second` **costs** `$1 per hour`**, and at** `50 TPS`**, it costs** `$0.3 per hour`**, making it a cost-effective solution for continuous operation. More details can be found on the [official Minimax page](https://www.minimax.io/news/minimax-m25).** Commenters highlight the potential game-changing nature of Minimax M2.5 due to its low operational costs compared to other models. There is also anticipation for the release of open weights on platforms like Hugging Face.

[Read more](https://www.latent.space/p/ainews-why-openai-should-build-slack)