Muse Spark Open: weights released 2026-10-15, an 816B total / 42B active MoE with a native multimodal Perception Encoder and a 1,048,576-token context window. Third-party API pricing is $1.25 input / $4.25 output per 1M tokens ($0.15 cached input) — identical to the closed Muse Spark 1.2 from two months earlier. In Meta's own comparison table, Muse Spark Open tops every listed model on JobBench (61.8) and MCP-Atlas (89.2), but scores only 39 on the AA Intelligence Index, behind GLM 5.3 (45) and Kimi K3 (44). It's also the first model of its size to run fully on two Apple M5 Ultra Mac Studios via MLX.
A TEE is a hardware-isolated execution environment meant to keep even the machine's operator from seeing what runs inside; remote attestation lets an outsider verify which program is running in that room. WhatsApp's Private Processing uses it for AI summaries. Meta's bounty page lists code execution (including bypassing code attestation), privilege escalation and VM escape at up to $300,000 each, data leaks at $150,000 and persistence at $10,000, invitation only, with lower caps for internal-only scenarios. Trail of Bits' review found 28 issues, 8 of them high severity, which shows the hard part of TEEs is implementation detail, not the concept.
Google multiplies the reward by 0.8, 1 or 1.2 depending on report quality across seven dimensions, and only counts what you provided before triage; later additions usually do not change it. Write to a one-page template (title, summary, preconditions, steps, impact, fix suggestion), then estimate hourly expected value as probability x amount / hours. A bigger ceiling can still lose to a small payout once the odds are low enough. Only Resolved is guaranteed to pay; duplicates and invalid reports are common. Every probability and hour count in this post is an assumption, not a statistic.
Alexandr Wang says Meta listed 100+ behaviors a personal agent needs, built an eval for each, and set two thresholds per row: a launch-blocking floor and a bar for 'good'. They shipped after the first all-green checkpoint and a hands-on check that it was clearly better; they also ran new-user testing. The method is reusable, but post-launch safety reports show all-green is not the same as safe.
Muse Wonder (API-callable) and Muse Wonder Full (Muse Code subscription only): released 2026-10-12, 1,048,576-token context, $4.00/1M input tokens (cached $0.20) / $20.00/1M output tokens — exactly matching Claude Opus 5.5. Meta's own benchmarks show Terminal-Bench 4.0-AA at 85.6 (Wonder) / 85.5 (Full), both ahead of Opus 5.5's 81.5, and SWE-Marathon at 78.8/79.0 versus Opus 5.5's 74.2. But independent evaluator Artificial Analysis puts Wonder at an Intelligence Index of 52 (9th of 219 models) and Wonder Full at 56 (4th of 699) — below both Meta's self-reported 56/59.0 and Opus 5.5's 58. This is Meta's first frontier-scale release since its August 2025 AI reorg, and the two checkpoints split capability asymmetrically: Wonder leads on AutomationBench and GDP.pdf, while Full leads by a wide margin on JobBench, Vals Finance, and MemoryBench.
Muse Spark is Meta's closed-source agentic model line: version 1.3 combines a 1M-token context, multimodal inputs, and long-horizon tool loops. Standard pricing is $1.25/$4.25 per 1M input/output tokens, while Contributor drops to $0.10/$0.20 in exchange for training rights. It is not the next Llama; it is a separate product line built around models, APIs, and coding agents.
Muse Voice Transcribe (muse-voice-transcribe-1.0): Meta Superintelligence Labs' first real-time audio perception model, launched 2026-09-01; closed-source, API-only, $0.18/hour of audio ($3.00 per 1,000 minutes); 3.1% final-transcript WER on streaming (#1 on Artificial Analysis AA-WER Streaming, ahead of Cartesia Ink-2's 3.4%), 0.16s delay from end-of-speech to final transcript; one model does ASR, 20+ speaker diarization, and endpointing together, replacing what used to require three separate systems
Muse Spark 1.2: 1M context window, input $1.25 / output $4.25 per 1M tokens (same as 1.1), AA Intelligence Index 57, GDPval-AA v2 Elo jumps 260 points to 1631 (5th overall), paired with Meta's first code agent Muse Code for long-running multi-agent collaboration
Llama is Meta's open-source LLM family, with the largest enterprise deployment footprint and the most mature ecosystem. Llama 4 Scout (10M context) and Maverick (17B active / 400B total MoE) are the current open multimodal benchmarks, but Meta pivoted to closed-source Muse Spark in April 2026—Llama 4 is likely the last major open Llama, and its license is not truly open (Llama 4 Community License, separate license required above 700M MAU).
In August 2026, Meta Superintelligence Labs released Muse Code beta. Closed-source static binary, Muse Spark 1.2 model, parallel persistent sub-agents with worktree isolation. The biggest controversy is pricing: Standard at $1.25/$4.25 per M tokens, or Contributor at $0.10/$0.20 — 20x cheaper, but your code enters Meta's training pipeline.
Northeastern tested 17,260 Android apps and found zero activating the microphone. In May 2026 the FTC ruled that Cox Media Group — the company that claimed to be listening — collected no voice data at all and was reselling data-broker email lists, settling for $930,000. The real pipelines are off-site event feedback, lookalike spillover, contact-graph uploads, and location brokers.
Top Silicon Valley companies are independently building internal AI coding agents that automate everything from a Slack message to a merged PR. This article deep-dives into architectures from Stripe, Ramp, Coinbase, and Spotify — including their 2026 growth numbers (Stripe 7,000+ PRs/week, Ramp 75% of merged PRs) — then expands to cover Google, Meta, Amazon, Uber, Shopify, PostHog, and more.