🚀 RELEASES & LAUNCHES
OpenAI launches Grid Protectors Program, pledging 1,000 MW of flexible power at Georgia campus
OpenAI launched the Grid Protectors Program to have its future data center campuses cut power use when local grids are strained, starting with its Effingham County, Georgia campus.- Aimed at utilities, regulators and communities near OpenAI compute campuses, plus residential and small-business ratepayers concerned about subsidizing AI power demand
- Under a 25-year Georgia Power agreement (approved August 2026), the planned ~3,200 MW Effingham County campus will provide up to 1,000 MW of flexible demand response — equivalent to the average electricity use of roughly 700,000 homes
- OpenAI commits to pay full infrastructure and electric-service costs for the Effingham project; campuses may also supply on-site generation or battery storage to support the grid
- Georgia Power projects ~$950 million in annual customer savings beginning 2029 from its large-load agreement portfolio, including at least $15/month benefit for a typical 1,000 kWh residential customer
- No other sites named yet; OpenAI notes grid flexibility supplements rather than replaces investment in new generation and transmission
OpenAI is merging ChatGPT's Chat and Work tabs into a single experience
OpenAI announced it will merge ChatGPT's Chat and Work tabs into one experience, with a shared usage pool for ChatGPT and Codex starting in December.- For ChatGPT users on Plus, Pro and Business plans who use both fast chat and agentic Work features.
- OpenAI's Tibo Sottiaux said Chat and Work will merge to cut complexity, after feedback that users loved Work's capabilities but preferred Chat's speed.
- Starting in December, Plus, Pro and Business usage allowances will be shared across ChatGPT and Codex, measured in tokens; Instant chat will not count against the allowance.
- OpenAI says most users should not hit reset limits more often than now, but the exact date, per-plan token amounts, and changes for Free, Go, Enterprise and Edu plans remain unpublished.
- The move follows user complaints about the July 9 desktop app redesign, and Greg Brockman had already promised the Work tab would fold into ChatGPT by year-end.
Sources 📎 Coverage: pasqualepillitteri.it · 👁 via TechCrunch AIISTA-DASLab releases 1-bit GSQ-RCO Coder build of DeepSeek-V4.1-Flash-1017
ISTA-DASLab released a GGUF 'Coder' build of DeepSeek-V4.1-Flash-1017 that prunes half the routed experts and quantizes the rest to an effective 1 bit per parameter, shrinking transformer weights to 72.1 GB.- Aimed at local-LLM users running coding and agentic workloads; built on DeepSeek's 763.2B-parameter MoE model (551.9B in the transformer, 384 experts per layer with 6 active per token).
- Removes 192 of 384 routed experts per layer via RCO-based KL-divergence selection, then applies GSQ/IQ2_XXS quantization to the retained 280.1B parameters (averaging 2.06 bits each).
- Transformer weights are 72.1 GB vs 138.2 GB for the unpruned IQ2_XXS build — an effective 1.05 bits per original parameter; Engram tables (196.6B params, 4.50 bpw) are shared and can stay on disk.
- Benchmarks: SWE-bench Verified retains 94.8% of the FP8/FP4 base (77.00 vs 81.20) and LiveCodeBench v6 retains 98.7% (88.57 vs 89.71); general-purpose users are pointed to the unpruned GSQ-RCO releases.
- Unsloth's independent benchmarking measured lower recovery than claimed (e.g. IQ1_M 97.24% vs 98.7% claimed), so real-world quality may vary; the release is experimental under an MIT license.
Hydrogen-1.0: open-weight GLM-5.3 fine-tune for science beats GPT-6-Astra on FrontierScience
Chenru Duan's team released Hydrogen-1.0, an open-weight scientific model post-trained from GLM-5.3 that scores 38.3% on FrontierScience-Research vs GPT-6-Astra's 33.3%.- Aimed at scientific researchers, the model was post-trained on internal scientific data, lab records, and discovery trajectories
- Hydrogen-1.0 is built on GLM-5.3 (Zai) and is the team's first open-weight model for scientific research
- It scores 38.3% on FrontierScience-Research, beating GPT-6-Astra's 33.3%
- Shows fine-tuned open-weight models can outperform frontier closed models on domain-specific benchmarks
AtomicChat releases uncensored DeepSeek-V4.1-Flash-1017 quants that run on a 12 GB GPU
AtomicChat released abliterated GGUF quants of DeepSeek-V4.1-Flash-1017 that cut refusals from ~85% to 0% while running on a 12 GB GPU via SSD streaming.- For local LLM users with modest hardware: 12 GB GPU VRAM, 64 GB RAM, and SSD streaming via the Strata Engine.
- AtomicChat edited only 82 dense tensors per file (attention output, shared expert down proj, Engram value proj), keeping the 384 routed experts byte-identical to ISTA-DASLab's GSQ-RCO quants.
- Measured on JailbreakBench: refusals drop from 88.9% to 0% (IQ1_M, 121 GB rung) and 85.2% to 0% (IQ2_XXS, 138 GB rung), including refusals during thinking; MMLU-500 stays at 80.6% with KL divergence 0.021 vs stock.
- The 552B-parameter MoE model is also available as an FP8 abliterated build hosted on Venice AI at $0.33/1M input tokens with vision, tools, and up to 512K context served.
- It matters because it puts a frontier-size uncensored model within reach of consumer hardware, though abliteration metrics are distributor-reported and users must supply their own moderation.
MiniMax thanks community builders who shrank M3.1 Flash quants from 252 GB to 93 GB
MiniMax publicly thanked community builders who produced quantized versions of its 428B M3.1 Flash model, cutting weights from 252.4 GB down to as small as 93 GB.- For local AI builders running the 428B-parameter, 23B-active MoE model on consumer and prosumer hardware (128 GB PCs, DGX Spark, 4x RTX 3090, Apple Silicon, AMD Instinct).
- MiniMax's Nov 1 X post credited Unsloth (2-bit Dynamic GGUF at 143.9 GB, 91.5% weighted top-1), ViC305's SAGE-EXL3 (108.64 GiB, 96.89% top-1, 24-30 tok/s at 48K context), PeasantSmith's Maya-XS (93 GB, 88.8% top-1 on tool calls), plus Mia-AiLab, oMLX, AMD Quark MXFP4, Atomic Chat GGUFs, and superalesha's custom engine (23-31 tok/s decode, 1120 tok/s prefill on 4x 3090).
- M3.1 Flash was trained with quantization-aware training (QAT) for attention and routed experts, making low-bit quants unusually faithful; Unsloth's 4-bit UD-Q4_K_XL hits 98.0% top-1 at 249.4 GB.
- The quants make a 1M-context multimodal agentic model runnable locally on hardware far below the ~252 GB needed for full weights, with per-quant fidelity numbers published for each build.
kvark-sparkle-1-release — r/LocalLLaMA
🔬 RESEARCH
Import AI 479: Realtime-Bench shows LLMs struggle at low-latency tasks; DIA space security report
Import AI 479 covers Realtime-Bench, a new benchmark showing frontier models perform poorly on real-time tasks, plus a DIA report on space warfare.- For AI agent developers and researchers: Realtime-Bench is a new benchmark of 50 interactive game and web tasks (30 web, 20 game) testing real-time decision-making, built by teams at UT Austin, Ohio State, UIUC, NVIDIA, Northwestern, and Steadyworks.
- It measures three skills: temporal state awareness (RT-Perceive), adaptive deliberation (RT-Deliberate), and time-aware action selection (RT-Act), under wall-clock, paused-clock, and token-clock conditions.
- Frontier models from OpenAI, Anthropic, Google, DeepSeek, and Moonshot were tested; GPT-6 Astra (high) scored the best wall-clock pass rate at just 36%, versus 70-80% with a paused clock.
- The newsletter also reviews the DIA's 'Challenges to Security in Space' report, detailing Chinese and Russian anti-satellite weapons, space nukes, and satellite-on-satellite encounters.
- Low Realtime-Bench scores flag where AI agents will break in latency-sensitive domains like auctions and ticketing; the DIA report matters as space becomes contested infrastructure for future data centers.
Simon Willison re-runs Census BTOS AI adoption data a year after Apollo's slowdown chart
Simon Willison revisited the Census Bureau's BTOS AI adoption data a year after Apollo's chart showed a slowdown among large companies.- For analysts and builders tracking US business AI adoption, the Census Bureau's biweekly BTOS survey samples ~200,000 of ~1.2 million businesses.
- Updated BTOS data (Dec 2025–May 2026) show overall business AI usage hovering between 17% and 20%, with 20–23% expecting to use AI within six months.
- 37% of firms with at least 250 employees reported using AI, and 32% of firms with 100–249 employees; AI use rose among firms with 20+ employees.
- Census revised the AI question wording in November 2025 from use 'in producing goods or services' to use 'in any business function.'
- Willison notes the Bureau's survey now operates under Executive Order 14434, the 'Super Intelligence' executive order.
Sources 📄 Source: census.gov · 📎 <https://simonwillison.net/2026/Nov/2/c