if the guy actually read my work he would see that what i actually said was
– Pure LLMs alone would not get us to AGI (even if they improved in some respects)
– Pure LLMs would continue to struggle with hallucinations and reasoning (they have)
– We need to incorporate elements
INNOVATION
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Pure LLMs Insufficient for AGI: Reasoning and Hallucination Challenges
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Anthropic Reaches $30B ARR Before Mythos Launch
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Amol (Head of Growth at @AnthropicAI
) just joined Twitter. Follow for free alpha. BTW, can you believe they hit $30B ARR before they even released Mythos? -
Meta Launches Muse AI Model Line After Llama 4 Setback
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META RETURNS TO THE BATTLE! After the failure of Llama 4, Meta has spent the last year completely reorienting its entire AI strategy, and today it finally unveils its first (private) model, aiming to go head-to-head with the big players through its new Muse model line
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Meta’s Muse Spark: Multimodal AI Model with Impressive Reasoning Benchmarks
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Meta Superintelligence Labsjust dropped Muse Spark, their first model after a full nine-month rebuild of their AI stack. the tl;dr (summary) It's a natively multimodal reasoning model that now powers Meta AI. It's competitive on reasoning and multimodal benchmarks, introduces a multi-agent "Contemplating mode," and Meta frames it as step one on a scaling ladder toward "personal superintelligence." Where it's strong: -Multimodal perception and visual reasoning (visual STEM, entity recognition, localization) -Health reasoning, built with input from 1,000+ physicians -Test-time reasoning efficiency, using thinking time penalties to compress reasoning tokens -Contemplating mode hits 58% on Humanity's Last Exam and 38% on FrontierScience Research, putting it in the ballpark of Gemini Deep Think and GPT Pro -Pretraining efficiency: reaches the same capability as Llama 4 Maverick with over 10x less compute Where it's weaker (Meta's own admission): -Long-horizon agentic systems -Coding workflows Key scaling findings: -RL compute scales smoothly with log-linear growth on pass@1 and pass@16 -Multi-agent orchestration scales performance without proportional latency increase -Phase transition behavior on AIME: the model first extends reasoning, then compresses it under length penalties, then extends again for higher accuracy My take: very good model, really surprised what meta offered here. And keep in mind: 99% of all instagram / facebook user dont need an LLM for doing academic reserach but for everyday reasoning. Well done, meta! Chubby♨️ (@kimmonismus) Lol what?! Meta has been cooking! These benchmarks are really freaking good holy!! — https://nitter.net/kimmonismus/status/2041918006779957407#m
→ View original post on X — @kimmonismus, 2026-04-08 16:42 UTC
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Production Intelligence: Competitive Edge in Industrial Manufacturing
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Production intelligence is becoming a competitive edge. How would you handle this? @IIoT_World @CRudinschi @agentic_factory @IotoneHQ @Softnet_Search @SmartIndustryUS
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Motion: AI Video Agent for Motion Design Launch
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introducing Motion, a video agent for tasteful motion design.
— Adish Jain ☕️ (@_adishj) 8 avril 2026
this launch video was made entirely with Motion. 👇🏽
comment "MOTION" to get early access + free credits.
tag @motion_so in any post on your X feed for a surprise.
here’s how it works + examples (thread): pic.twitter.com/CmeMgYPW6jintroducing Motion, a video agent for tasteful motion design. this launch video was made entirely with Motion. 👇🏽 comment "MOTION" to get early access + free credits. tag @motion_so in any post on your X feed for a surprise. here’s how it works + examples (thread):
→ View original post on X — @scobleizer, 2026-04-08 16:38 UTC
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Meta’s New Muse Spark Model Delivers Impressive Benchmark Results
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Lol what?! Meta has been cooking! These benchmarks are really freaking good holy!! Alexandr Wang (@alexandr_wang) 1/ today we're releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. 🧵 — https://nitter.net/alexandr_wang/status/2041909376508985381#m
→ View original post on X — @kimmonismus, 2026-04-08 16:36 UTC
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Meta’s Muse Spark Rises to 4th in AI Arena
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Meta jumped from last to the 4th place on Artificial Analysis arena with its newly released Muse Spark model. It also appears to be token efficient for its level of intelligence.
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LFM2.5-VL-450M Vision-Language Model Now Available
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Available today on @huggingface! huggingface.co/LiquidAI/LFM2…
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Liquid AI Releases LFM2.5-VL-450M Tiny Vision-Language Model
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New tiny VLM: LFM2.5-VL-450M
— Maxime Labonne @ ICLR (@maximelabonne) 8 avril 2026
> Supports bounding box prediction, object detection, and function calling
> Improved multilingual capabilities across 9 languages
> Enhanced instruction following for vision and text tasks https://t.co/diQjDO9RCX pic.twitter.com/hJqlV6YFcDNew tiny VLM: LFM2.5-VL-450M > Supports bounding box prediction, object detection, and function calling > Improved multilingual capabilities across 9 languages > Enhanced instruction following for vision and text tasks Liquid AI (@liquidai) Today, we release LFM2.5-VL-450M, a vision-language model built for real-time reasoning on edge devices. It processes a 512×512 image and returns structured outputs in ~240ms on-device. — https://nitter.net/liquidai/status/2041912441060143251#m