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Meta launches Muse Voice Transcribe for real-time voice dictation on Mac. * 9to5Mac By Zac HallSep 1, 2026, 1:00 pm141 ptsTrending Meta is launching Muse Voice Transcribe, its first real-time audio perception model. It brings multilingual, streaming transcription to Meta AI for Mac, Muse Code, and developers through the Meta Model API. more... Read Article Share Article * email * x.com * facebook * pocket * reddit * tumblr * linkedin * pinterest

Meta's EvoHarness-RL teaches smaller models to self-manage task execution. [email protected] (Ben Dickson) Sep 1, 2026 · about 10 hours ago Researchers at Meta AI and University of Illinois Urbana-Champaign developed EvoHarness-RL, a training framework that enables smaller AI models to perform complex, long-horizon tasks by learning to dynamically manage their execution environment rather than following rigid, manually-coded instructions. The approach consolidates agent support systems into a unified Belief, Progress, and Experience workspace, allowing models to independently decide when and how to consult external state during workflows. This addresses a key limitation in current agentic systems where manual prompts and static memory structures require extensive retuning for each model upgrade. Tl;dr. * Meta AI and UIUC researchers introduced EvoHarness-RL, a training technique that teaches AI agents to optimize their use of execution harnesses for complex tasks * The framework consolidates belief tracking, progress monitoring, and experience management into a single unified interface rather than relying on rigid, manually-coded logic * Current agent systems degrade performance over long tasks because append-only memory accumulates outdated conclusions and irrelevant information * The approach reduces engineering overhead by eliminating the need to manually retune prompts, memory designs, and sandbox configurations for each model upgrade Why it matters. Long-horizon AI agent tasks require dynamic management of execution state, but current systems rely on manual prompts and static memory that become liabilities as tasks grow complex. EvoHarness-RL trains models to actively manage their own environmental understanding, updating and compressing information in real time rather than accumulating it blindly. This shifts the burden from human engineers to the model itself, making agent systems more adaptable and scalable. Business impact. Enterprise workflows like data migration, customer record management, and complex API orchestration require agents that can recover from errors, track progress across hours-long tasks, and adapt to changing conditions without constant human intervention. Current approaches require extensive manual configuration for each model version, creating maintenance overhead. EvoHarness-RL reduces this friction by enabling models to learn optimal harness behavior, lowering the engineering cost of deploying and upgrading agent systems. Key implications. * Smaller models trained with EvoHarness-RL may handle enterprise automation tasks previously requiring larger, more expensive frontier models, shifting cost economics in AI deployment * The framework addresses a critical gap in current agent architectures where long-term skill curation and real-time state tracking operate separately, potentially improving reliability of multi-step workflows * Manual harness configuration becomes a bottleneck as model capabilities improve, making automated harness optimization a competitive advantage for organizations deploying AI agents at scale What to watch. Monitor whether EvoHarness-RL generalizes across different model sizes and task domains, and whether it reduces the engineering overhead organizations currently face when upgrading their deployed agents. Watch for adoption by enterprises running complex automation workflows to see if the framework delivers on its promise of reducing model-specific tuning cycles. Track whether this approach influences how other labs design agent training methodologies.

Meta's advertising revenue grew 27% in the second quarter, reaching $59.4bn and closing to within $3.9bn of Google Search. Bernstein analysts predict Meta could surpass Google's search business by year-end 2026. The growth is driven by Meta's Advantage+ AI advertising platform, which runs at a $60bn annualised rate. Advertisers using the automated system generate an average return of $4.52 for every dollar spent, roughly 22% more than manual campaigns. Meta's total revenue climbed 28% year-over-year to $60.8bn. Ad impressions increased 14%, whilst the average price per ad rose 12%. The company spent $31.1bn on capital expenditure in the quarter, reducing free cash flow to $784m. Bernstein says Meta captured nearly half of every incremental digital advertising dollar in Q2.