Liquid AI releases LFM2.5-24B-A2B, a reasoning model for on-device agents
Liquid AI introduced LFM2.5-24B-A2B, a 24B-parameter reasoning model with just 2B active parameters, built to run fast, private agents on local hardware.
AI Desk1 min read
Section A
News & Industry
Breaking AI news, model releases, big tech moves, funding and the business of AI.
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Mistral launches Timestral, its first family of time-series forecasting models
Mistral released Timestral, four zero-shot forecasting models from 5M to 209M parameters that it says rank first on the GIFT-Eval, TIME and fev-bench leaderboards.
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Meta releases Muse Spark Open, an 816B open-weight multimodal model for coding
Meta published Muse Spark Open, a mixture-of-experts model with roughly 816B parameters, 42B active per token, multimodal input and a 1M-token context window.
AI Desk1 min read
Reflection releases Beam, a 501B open-weight coding model under Apache 2.0
Reflection, a new US lab, put out Beam: a 501B mixture-of-experts model with 23B active parameters, a 1M-token context and an Apache 2.0 license.
AI Desk1 min read
Weekly RecapDigest
Weekly Recap: Gemini 4 Argon, Mistral Large 4 and a flood of open-weight models
The week of October 12 to 18, 2026 in AI: Google ships Gemini 4 Argon, Mistral releases Large 4, NVIDIA launches RTX Spark laptops and open-weight giants arrive.
AI Desk1 min read
Daily DigestDigest
Daily Digest: Two giant open-weight models, Timestral forecasting and RTX Spark laptops
The AI stories that mattered on October 18, 2026: Reflection Beam and Meta Muse Spark Open, Mistral Timestral, FLUX.3, NVIDIA RTX Spark and more.
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Preview, beta, GA: how AI product launches actually work
Waitlists, research previews, staged rollouts and general availability: a guide to the stages every AI product goes through and what each one means for users.
AI Desk3 min read
How Big Tech plays the AI game: five strategies compared
Microsoft, Google, Meta, Amazon and Apple are all betting on AI, but in very different ways. A guide to their partnerships, models, chips and distribution.
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Why Google, Amazon, Microsoft and Meta design their own AI chips
TPU, Trainium, Maia and MTIA: the biggest AI buyers are designing custom silicon. Here is why, what these chips do, and why NVIDIA still dominates.
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From seed to mega-round: how AI startup funding works
Why AI startups raise unusually large rounds, what seed, Series A and growth rounds mean, and how compute costs change the economics of venture funding.
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Compute is the new capital: why AI startups raise GPUs, not just cash
For AI startups, access to GPUs can matter more than money. How cloud deals, credits and chip reservations shape who can build frontier models.
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A practical framework for rolling out generative AI in a company
Governance, use-case selection, pilots, measurement and scale: a step-by-step framework for introducing generative AI at work without losing control.
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Build, buy or fine-tune? How companies choose their AI approach
Off-the-shelf assistants, API integrations, retrieval-augmented generation, fine-tuning or your own model: the trade-offs in cost, control and time to value.
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Why AI is driving record spending on data centers and power
Chips, buildings, cooling and electricity: what AI infrastructure spending pays for, why it keeps growing, and what the energy demand projections say.
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Will AI boost productivity? What the economic research says
Field experiments show large gains for some workers; macro estimates are far more cautious. A plain-English guide to the evidence on AI and productivity.
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Cloud, chips and distribution: the anatomy of an AI partnership
Why AI labs sign deals with cloud providers, chipmakers and device makers, what each side gets, and the patterns behind the biggest partnerships in AI.
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