Meta / Llama

Meta released full training code for Brain2Qwerty v2, and BCBL released the v1 dataset. The system uses MEG while participants type, decoding sentences end-to-end from raw brain signals with no hand-crafted features. Trained on ~22,000 sentences from 9 volunteers, it averages 61% word accuracy—the best participant hits 78%, with over half of sentences decoded at ≤1 word error. Other non-invasive methods sit at 8%. Accuracy improves log-linearly with data volume, so scaling alone may close the gap to invasive approaches. The catch: each person still needs 10 hours of MEG recording.

Meta ran an internal project called 'Cannes' through contractor Covalen, active at least until April 2026. Contractors created under-18 accounts and sent prompts about self-harm, eating disorders, and drugs to ChatGPT, Gemini, and Character.AI, then copied responses into spreadsheets. A single round in August 2025 involved over 45,000 prompts, many written from the perspective of children in crisis. Meta called it responsible industry-standard safety testing and said it didn't use the responses to train its own models, but documents reviewed by WIRED don't show what Meta actually did with the data. The tested companies had no prior knowledge: Character.AI said it violated its terms, OpenAI is investigating, and Google said it didn't approve the tests and can't determine if terms were broken. The backdrop includes several teen suicides linked to AI chatbots and a UK survey finding 64% of kids aged 9–17 have used chatbots, with effective age verification mostly absent.

Google has capped Meta's access to its Gemini models because Meta requested more compute than Google could supply, the FT reports. Several other clients are also affected, though to a lesser extent. The post doesn't spell out the specific limits, which Gemini versions are involved, or what Meta uses them for.

Meta launched MCI in April 2026 to record keystrokes, mouse movements, and screen content from employee computers for AI training. The program was paused in late June after a permissions misconfiguration exposed screen recordings, private chats, and performance data across the internal network. Over 1,600 employees signed a petition; the program had no opt-out. The pause was not driven by regulators or training failure—it was an internal security breach that broke employee trust. Zuckerberg framed it as a third-wave training data strategy: let models learn by watching smart people work. Execution failed on informed consent, data minimization, and access control.

Meta replaced roughly half of human moderation with LLMs in 2025 and aims to push that above 90% for some content types by year-end. The company claims its models make 13% fewer errors and catch 10% more violations than humans, saving billions annually. Employees counter that the models still remove or shadow-ban harmless content and that oversight is insufficient for such a fast rollout. Behind the scenes, Meta is also swapping from Google Gemini to its own Muse Spark model, trained on past human moderation decisions.

Claude Code creator Boris Cherny told Meta's @Scale conference that loops are the next big step after agents. A loop authorizes a swarm of agents to run continuously in the background, finding work and submitting pull requests on their own. Cherny runs two loops himself: one improves code architecture, another merges duplicate abstractions. He says the shift is as big as going from hand-written code to agent-written code. The post doesn't provide a technical definition of loops or quantitative results from real deployments.

Over 1,600 Meta employees signed an open letter demanding the company stop collecting keystrokes, mouse movements, screen content, and other computer-use data under its 'Model Capability Initiative' for AI training. The letter says leadership disclosed no completed privacy reviews, offered executives an opt-out, and failed to address how sensitive data like SSNs would be protected. It cites Meta's €91M GDPR fine for storing plaintext passwords and a March 2026 incident where an AI agent caused a sensitive data leak.

Ian Barber compares Llama 3 and Nemotron 3 Ultra architectures, showing modern LLMs now pack multiple attention variants, MoE routing, multimodal encoders, and multi-GPU inference. The pattern mirrors how recsys moved from clean two-tower models to complex engineering. The core tension: you can't afford to test a new attention variant without at least partial kernel fusion, but hand-fusing every candidate is too expensive. His takeaway is to design for composability and verifiability upfront, like PyTorch's FlexAttention, so the research loop stays cheap.

Gergely Orosz traces Meta's engineering culture from 'move fast and break things' to 'move fast with stable infra,' then argues that since April 2026 leadership has been systematically wrecking it. The core shift: treating software engineering as a cost center rather than a profit center, forcing engineers into AI data-labeling work, crushing morale, and triggering a major outage. The piece cites the 2012 'little red book' slogans and a 2022 deep-dive on Meta's culture, but does not disclose internal decision-making details—it relies on external observation and insider accounts.

Meta's internal leaderboard Claudeonomics tracked ~85,000 employees' token usage, hitting 73.7 trillion tokens in 30 days—billions of dollars. Uber burned its full-year AI coding budget in four months after giving 5,000 engineers Claude Code. The subsidy cycle is ending: Claude Code's $200/month subscription masks heavy-user costs of ~$5,000/month, roughly 25x the subscription price. Meta's June memo set 2027 as the year for structured token budgets and allocation tools. The article maps AI cost management to four management moves: model routing instead of tiered staffing, context engineering instead of bounded scope for new hires, prompt caching instead of codifying SOPs, and measuring output instead of token count. Jellyfish's analysis of 12,000 developers found the heaviest users burned 10x tokens per PR with only 2x throughput; per-PR cost jumped from $0.28 to $89.32 with no quality gain. Bosworth championed unlimited token burning in April, then wrote in June that token usage alone is not a measure of impact of any kind.

Meta has cut Manus off from internal systems and halted data sharing, the most concrete step since Beijing ordered the deal reversed on national security grounds. Manus co-founders have held early talks to raise roughly $1 billion to buy the company back from Meta.

Zuckerberg said in an internal memo that Meta's AI restructuring moved too fast and caused organizational strain. In May, the company cut 10% of its global workforce and reassigned roughly 7,000 people to AI-related projects, some into model training. He admitted the company will 'almost certainly make more mistakes' but ruled out another company-wide layoff this year. A new Applied AI Engineering org runs a flat structure with a 50:1 IC-to-manager ratio, which Meta now plans to dial back. A large hackathon is set for July to boost cross-team collaboration and model development.

This TechCrunch podcast episode covers the IPO market heating up with a new acronym: MANGOS — Meta (or Microsoft), Anthropic, Nvidia, Google, OpenAI, and SpaceX. Half of that group is heading to public markets in the same window, testing investor appetite and valuations. The post is an RSS snippet and doesn't disclose specific timelines or valuation ranges.

The European Commission issued an interim measure on June 9, ordering Meta to give third-party general-purpose AI assistants free access to WhatsApp until its antitrust investigation concludes. Meta banned external AI from the WhatsApp for Business API in October 2025, then switched to a paid model in March 2026. The Commission sees the paid access as effectively continuing the ban and harming smaller competitors.

Meta confirmed that thousands of Instagram accounts were hacked through abuse of its AI chatbot; the RSS snippet does not disclose the exploit mechanism, timeline, affected regions, or remediation status.

Meta is considering selling tens of billions of dollars in new stock to finance AI infrastructure; the post names a Google deal in the title but does not disclose its size, timing, or pricing.

Meta pushed face-recognition code named NameTag into its smart-glasses companion app, which has more than 50 million downloads; the feature uses three AI models to convert faces into local face templates and match them against a phone database.

404 Media reported on June 5 that attackers used Meta’s AI customer support agent to link Instagram accounts to attacker-controlled email addresses; the article says the only extra condition was using a VPN matching the account owner’s location.

Gloria Mark’s device-use studies found average adult attention spans fell from about 2.5 minutes in 2003 to 47 seconds across 2014–2020, and she warned that ChatGPT, Claude, and Gemini shift summarizing and evaluation work away from users’ own cognitive processing.

Meta made its WhatsApp Business AI agent available to merchants globally and will charge businesses based on model token usage; the post does not disclose pricing, model names, or a market-by-market availability list.