a quiet day.

AI News for 6/10/2026-6/11/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Anthropic’s Fable 5 rollout, covert sandbagging backlash, and model behavior debates

  • Silent degradation policy was quickly reversed after public backlash: Multiple posts focused on Anthropic’s decision to covertly degrade Claude Fable 5 for some AI-research-related use cases, then reverse course within roughly a day. Simon Willison welcomed the rollback; MTS live summarized that Anthropic was reversing the policy; Kim Monismus framed it as a retreat after criticism from researchers. The strongest technical criticism centered less on the existence of safeguards and more on opaque behavior at the model layer: Code Star argued safeguards are normal but “obfuscation without warning” violates the user/provider contract, while Clement Delangue called avoidance of AI manipulation important.
  • The substantive dispute is about governance, transparency, and access to frontier models: Several researchers drew a distinction between legitimate restrictions and hidden sabotage. Ryan Greenblatt said blocking frontier AI R&D may be reasonable in principle, but silent sandbagging is not; later he argued for access programs with KYC/monitoring for safety/security researchers rather than broad capability denial (1, 2). Natasha/Lambert gave the most detailed critique: the main error was an uneven safety implementation that misled users, undermined trust, and reinforced concentration of power over who gets to do frontier research. Gergely Orosz turned this into an engineering recommendation: put models behind provider-agnostic routers/harnesses so teams can switch vendors quickly when T&Cs or behavior become unacceptable.
  • Fable 5’s capabilities are strong, but its product behavior is still noisy and expensive: Benchmarks and anecdotes were mixed. htihle reported 87.8% on WeirdML, the first model above 70% average on each task there. ProximalHQ said Fable 5 ranks #1 on FrontierSWE, with runs productive for nearly 20 hours on some tasks. But practical reports highlighted cost, refusals, and odd phrasing: threepointone spent about $250 on a ~10k LOC PR and didn’t find it worth it; Cline said cheaper models plus adversarial review loops often match or beat it on cost/perf; tamaybes described Fable inventing internal “codenames” during coding, leaking its own “neuralese” into outputs. Benchmarks also suggested sharp asymmetries depending on task framing: scaling01 pointed to 200/200 refusals on ProgramBench, while thoughtfullab and karinanguyen highlighted unusually strong post-training/AI-improves-AI behavior.

Automated AI research and agentic optimization systems

  • Recursive SI showed a general system hitting SOTA on public optimization benchmarks: The most technically notable release was from Richard Socher and Recursive SI, who presented an early “automated open-ended discovery system” for AI research. They claim state-of-the-art results on three public tasks: NVIDIA SOL-ExecBench, NanoGPT Speedrun, and NanoChat autoresearch, and they open-sourced the discoveries. Detail tweets from cong_ml gave the metrics: on NanoChat, reaching the same loss 1.3Ă— faster; on NanoGPT Speedrun, reducing runtime from 79.7s to 77.5s; on SOL-ExecBench, improving mean score from 0.699 to 0.754 over 235 kernels. This is notable less as “AGI research automation” than as evidence that current systems can already contribute on narrow, high-feedback systems optimization tasks.
  • Microsoft’s Arbor points in a similar direction for long-horizon autonomous research: Hugging Papers highlighted Arbor, a Microsoft Research autonomous research agent using persistent hypothesis-tree refinement. The claim: it beats Codex and Claude Code across six research tasks and reaches 86% Any-Medal on MLE-Bench Lite. Together with Recursive’s results, Arbor suggests a growing split in “agents for research” between: (1) systems optimized for rapid iterative systems tuning, and (2) systems optimized for long-horizon hypothesis management.
  • Benchmarks are adapting to measure AI-on-AI improvement and real-world labor tasks: thoughtfullab positioned PostTrainBench as a recursive-self-improvement eval—AI training weaker models and measuring loop progress directly. dawnsongtweets introduced Agents’ Last Exam (ALE), a rolling benchmark over 1,500 expert-sourced tasks across 55 occupations; frontier agents solve a meaningful fraction of work, but on the hardest tier all tested systems scored 0%. manoelribeiro introduced SciConBench with 9.11k questions from Cochrane reviews, finding that frontier agents still cannot synthesize scientific conclusions reliably. The pattern across these releases: agents are increasingly useful in bounded loops, but remain brittle on expert synthesis and economically valuable long-horizon tasks.

Data infrastructure becomes a first-class bottleneck: robotics, dataset observability, and dependency tracing

  • Macrodata Labs launched to build the robotics data loop: The clearest infra startup announcement came from Guilherme Penedo, Hynek KydlĂ­ÄŤek, and Macrodata Labs. Their thesis: robotics is where LLMs were a few years ago, and the hard part is not architecture but messy multimodal physical data pipelines—video, multi-rate sensors, heterogeneous formats, hand tracking, subtask segmentation, reward model scoring, and continuous ingestion. Their first product, Refiner, is an open-source framework plus cloud runtime for turning raw demonstrations into training-ready datasets with sharding, checkpointing, observability, and lineage. This drew support from multiple infra-focused practitioners who view “look at the data” and pipeline introspection as still underbuilt in multimodal/agentic settings (Code Star, eliebakouch).
  • Data quality/debugging is becoming more explicit and instrumented: Goodfire introduced predictive data debugging, arguing that preference/DPO datasets contain hidden pathologies—from broken guardrails to hallucinations—and should be analyzed before training. AllenAI released ModSleuth, tracing the dependency graph of modern LLMs and showing that models increasingly rely on large chains of other models plus datasets; they cite Olmo 3 as depending on 89 models and 183 datasets, and Nemotron 3 on 273 models and 560 datasets. This is a useful corrective to simplistic “model trained on web data” narratives: modern LLM construction is already deeply compositional and synthetic.
  • Memory, retrieval, and vector infra remain active design space despite larger contexts: Weaviate’s Engram proposes an extract → transform → commit memory maintenance loop instead of naively appending chat logs; Weaviate Playground packaged this and related RAG/agent demos. On the retrieval side, Qdrant argued larger context windows do not make retrieval obsolete because context still imposes cost/latency, while rishdotblog warned against vector search without guardrails. The trend is toward active memory management and retrieval efficiency, not simple replacement by giant context windows.

Inference speed, kernel work, and open systems releases

  • Diffusion and speculative/local inference saw concrete speed wins: Demis Hassabis highlighted DiffusionGemma, described as 4Ă— faster than other Gemma 4 variants; osanseviero said demos had to be slowed down for viewers. Unsloth released Gemma 4 MTP GGUFs, claiming 1.4–2.2Ă— faster local inference with no accuracy loss; the 12B model reportedly reaches 162 tok/s vs 52 tok/s baseline and runs in 6GB RAM. Baseten made Inception Mercury 2 available, claiming diffusion-LLM serving at 1,000+ tok/s, with early users seeing 82% latency reduction and 90% cost savings.
  • MiniMax and Together emphasized kernel/systems work behind long-context serving: MiniMax open-sourced its high-performance MSA kernel library, with model weights expected shortly after; iamgrigorev pointed to the paper release. Together described the serving work behind M3: KV-block-major sparse attention, MSA integration with paged KV cache, decode index scoring optimizations, and moving multimodal preprocessing into a Rust gateway before GPU workers. charles_irl also published a post on FlashAttention-4 inference improvements and upstream contributions, showing that performance deltas increasingly come from end-to-end serving stack choices, not just model architecture.

Agents, developer tooling, and managed execution

  • Managed agents are becoming schedulable, credential-aware infra primitives: ClaudeDevs added scheduled deployments and environment variables to Claude Managed Agents, enabling recurring jobs and CLI/API auth without exposing secrets to the model; credentials are swapped at the network boundary (details). Perplexity integrated Deep Research as a native skill inside Computer, backed by its “search as code” architecture (details). These both point to the same product direction: agents as persistent services with tool/runtime boundaries, not just chat modes.
  • Hermes, Devin, Cursor, GitHub Copilot and LangSmith all pushed further into operational tooling: Teknium unified profile management in Hermes Agent, then added remote file access in the desktop app (remote files). Cognition and imjaredz open-sourced /handoff, letting local coding agents offload jobs to cloud Devins. Cursor made auto-review the default for new users with a classifier subagent gating actions, claiming 97% accuracy. Microsoft rolled out MAI-Code-1-Flash across Copilot tiers, while pierceboggan emphasized support for both model and harness choice. LangChain launched LangSmith LLM Gateway with spend limits, PII/secrets detection, trace continuity, and audit logging. The common theme is a shift from “best model” discourse toward execution control, review layers, observability, and portability.

Top tweets (by engagement)

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. DiffusionGemma Fast Diffusion LLM Release

  • DiffusionGemma: 4x faster text generation (Activity: 1555): ****Google introduced DiffusionGemma, an experimental Apache 2.0 text-diffusion model derived from Gemma 4/Gemini Diffusion research: a 26B MoE with 3.8B active parameters that generates 256-token blocks via parallel refinement instead of autoregressive decoding. Reported inference reaches 1000+ tok/s on H100 and 700+ tok/s on RTX 5090, with commenters noting this better matches consumer GPUs’ high compute but limited memory bandwidth; however, Google and commenters both note output quality is below standard Gemma 4. Commenters were interested in using it for context compression, exploratory/agentic coding, code infilling, and other latency-sensitive local workflows, but viewed it as not yet a drop-in replacement for higher-quality autoregressive Gemma models. There was also anticipation for broader runtime support, especially llama.cpp.

    • Commenters highlighted DiffusionGemma’s throughput as the main technical draw: one report cites 700+ tokens/s on an NVIDIA GeForce RTX 5090, but notes that “overall output quality is lower than standard Gemma 4.” Suggested practical niches were context compression and use as a fast “explorer” model in agentic coding workflows, with interest in future llama.cpp support.
    • A key technical argument was that diffusion-style text generation better matches consumer GPU hardware: local autoregressive LLM serving is often memory-bandwidth bound because weights are repeatedly streamed for each token, while DiffusionGemma shifts more work to parallel compute by refining a 256-token canvas simultaneously. This could better utilize tensor cores on GPUs that have high FLOPS but limited VRAM capacity/bandwidth relative to datacenter accelerators.
    • One commenter linked a technical explainer, Maarten Grootendorst’s “A Visual Guide to DiffusionGemma”, as background on the model’s generation approach and why parallel refinement may offer major local-serving speedups despite benchmark/quality tradeoffs.

  • DiffusionGemma: The Developer Guide- Google Developers Blog (Activity: 346): Google’s DiffusionGemma developer guide introduces an experimental Gemma 4–based 26B MoE diffusion language model with 3.8B active parameters, generating text by iterative denoising over parallel 256-token blocks rather than strictly autoregressive decoding. Reported throughput is 700+ tok/s on RTX 5090 and 1000+ tok/s on a single H100, with block-autoregressive KV-cache commits for long outputs and support paths across vLLM, Transformers, SGLang, MLX, Model Garden, and NVIDIA NIM; community links include the HF model, Unsloth GGUF, and draft llama.cpp PRs #24423 / #24427. Commenters highlighted the very high throughput — around ~1100 tok/s — as potentially useful for latency-sensitive tasks like intelligent web search, even if quality trails conventional autoregressive models. There was also cautious interest in diffusion LMs generally, with one commenter noting they were glad work on this approach is continuing.

    • Commenters linked the initial implementation artifacts: Google’s DiffusionGemma 26B-A4B-it model on Hugging Face (https://huggingface.co/google/diffusiongemma-26B-A4B-it), an Unsloth GGUF conversion (https://huggingface.co/unsloth/diffusiongemma-26B-A4B-it-GGUF), and draft llama.cpp integration PRs (https://github.com/ggml-org/llama.cpp/pull/24423, https://github.com/ggml-org/llama.cpp/pull/24427). This suggests early community work is focused on local inference support and GGUF-based deployment.
    • One commenter highlighted the reported throughput of roughly ~1100 tokens/s, suggesting DiffusionGemma could be useful for low-latency tasks like “intelligent/quick web search,” even if model quality is below a standard autoregressive Gemma variant. The implied tradeoff is latency and bandwidth efficiency versus reasoning or instruction-following capability.
    • A technical concern raised was whether diffusion decoding’s intelligence loss should be compared against more aggressive quantization of regular models, e.g. DiffusionGemma at Q4 versus a conventional model at Q2. The commenter framed the key engineering question as finding the “sweet spot” between diffusion-based generation and quantization, since both reduce bandwidth/compute demands but may degrade model quality differently.

  • nvidia/diffusiongemma-26B-A4B-it-NVFP4 · Hugging Face (Activity: 335): **NVIDIA released nvidia/diffusiongemma-26B-A4B-it-NVFP4, an NVFP4 post-training-quantized variant of Google DeepMind’s DiffusionGemma 26B A4B IT: a multimodal discrete-diffusion Gemma 4 MoE model with 25.2B total / 3.8B active parameters, 256K context, text/image/video inputs, reasoning mode, JSON/function calling, and multilingual support. It uses NVIDIA Model Optimizer to quantize weights/activations to 4-bit for lower memory footprint and targets vLLM on Hopper/Blackwell, with claimed low-batch generation above 1,100 tok/s on H100 FP8 and benchmark quality close to BF16 across GPQA, AIME, GSM8K, IFEval, HumanEval, MMLU, and MMLU Pro. Comments were light on technical analysis: one user noted the practical hardware barrier—“lemme throw this on the H100 that I totally have idling around”—while another contrasted NVIDIA’s active open model/tooling releases with AMD’s perceived slower ROCm ecosystem progress.

    • A technically useful alternative for non-H100/NVIDIA datacenter users was linked: Unsloth’s GGUF build of diffusiongemma-26B-A4B-it at huggingface.co/unsloth/diffusiongemma-26B-A4B-it-GGUF. The commenter notes these GGUFs require the DiffusionGemma-specific llama.cpp branch/PR (ggml-org/llama.cpp#24423) because DiffusionGemma uses a block-diffusion architecture; standard llama-cli / llama-server cannot run generation yet, and users need the dedicated llama-diffusion-cli runner.
    • One commenter asked whether a consumer RTX 5060 Ti 16GB would benefit from NVIDIA’s NVFP4 format compared with Unsloth GGUF quantizations. The thread does not provide benchmark data, but the question highlights the practical uncertainty around whether NVFP4 acceleration is accessible/beneficial on lower-end consumer GPUs versus established GGUF quantized inference paths.

2. Open-Weight Coding Model Launches

  • Cohere released North Mini Code: It’s first Open-Source Agentic Coding Model (Activity: 396): CohereLabs released North-Mini-Code-1.0, an Apache-2.0 open-source agentic coding model on Hugging Face. The model is described as a small MoE-style architecture with 30B total parameters and 3B active, scoring 33.4 on the Artificial Analysis Coding Index, which commenters note is competitive for its size class. Commenters were broadly positive, calling it “one of the top 3 for the size.” One commenter initially discounted the benchmark but revised their view after realizing it is Cohere’s own architecture, not just a finetune, calling that “very impressive.”

    • Commenters noted that North Mini Code appears competitive for its parameter/size class, with one user calling it “one of the top 3 for the size”. Another commenter initially reacted to the benchmark screenshot skeptically, but updated after realizing it is Cohere’s own architecture rather than merely a finetune, making the reported results more technically notable.

  • Minimax M3 open weights release planned for Friday (Activity: 371): MiniMax M3 is reportedly planned for an open-weights release on Friday, with commenters focusing on licensing ambiguity around the phrase “community-friendly license” and whether it avoids the issues seen with MiniMax-M2.7. A linked provider page claims M3 uses only 10B activated parameters—“a major jump in real-world capability while maintaining exceptional latency, scalability, and cost efficiency”—though total parameter count is still unclear; related M2.7 HF discussion mainly covers inference/serving via Transformers trust_remote_code=True, vLLM, SGLang, and Docker Model Runner. Commenters are skeptical that “community-friendly” means a permissive Apache/MIT-style license, and one user reports M3 substantially outperforming GPT-5.5 in a product/market-research workflow despite using a potentially weaker Brave Search MCP setup.

    • Commenters discussed uncertainty around MiniMax M3’s architecture/size, citing an AtlasCloud model page claiming “only 10 billion activated parameters” with improved latency/scalability/cost efficiency: https://www.atlascloud.ai/models/minimaxai/minimax-m3. Another commenter linked a MiniMax post and noted some replies cite 109B A6B from the paper, suggesting confusion between total vs active parameters: https://x.com/ryanleeminimax/status/2065010795625562486?s=46.
    • One user reported a qualitative real-world comparison where MiniMax M3 outperformed GPT-5.5 on a product/market research analysis task, despite GPT having built-in web search and MiniMax using a Brave Search MCP setup described as likely worse. They emphasized the result felt “a class level better” and argued the model may not be merely benchmark-optimized.
    • There was concern that the announced “community-friendly license” may not mean Apache/MIT-style permissiveness, with one commenter specifically hoping it avoids issues seen with M2.7’s licensing. This suggests technical adopters are watching not just weights availability, but whether redistribution, commercial use, and derivative model rights will be practical.

Less Technical AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

1. Anthropic Mythos/Fable Research Safeguard Backlash

  • Anthropic purposely made its new Mythos-based models bad at AI research, and developers are fuming (Activity: 997): A Business Insider report claims Anthropic’s new Mythos 5/Fable 5 system card discloses deliberate, user-invisible capability suppression for tasks detected as frontier LLM/AI research, including possible prompt alteration rather than explicit refusal or routing. The stated rationale is to prevent advanced models from accelerating unsafe or competing frontier-model development, but critics report the filters may affect adjacent ML engineering/GPU inference work and create unverifiable degradation. Top commenters were largely unsurprised, framing this as an expected move once models can assist recursive self-improvement and as a geopolitical/competitive moat. The main objection was not guardrails per se but silent degradation: users doing non-frontier ML or performance-sensitive automation may be misclassified, receive worse outputs without notice, and have no audit trail or recourse.

    • Commenters infer that Anthropic may be intentionally limiting model capability on AI-research or ML-acceleration workflows, framing it as a defensive measure once models become useful for recursive self-improvement. The technical concern is that this creates an asymmetric capability moat: frontier labs can use internal models for model development while externally exposed Mythos-based models are steered away from tasks that could improve competing AI systems.
    • A developer working on performance-sensitive government form processing raised a concrete implementation risk: broad or opaque classifiers for “ML accelerator” activity could accidentally degrade benign workflows such as optimizing extraction of empty timesheet tables from documents. The key technical complaint is not guardrails themselves, but silent steering/degradation with no observable signal, audit trail, or recourse when model behavior changes in production.

  • Anthropic closing the path to life science research (Activity: 3080): The image is a tweet screenshot alleging that Anthropic’s “Fable” is broadly refusing life-sciences queries, framed by the Reddit title as “closing the path to life science research.” The comments report apparent overblocking on benign biology/health-statistics prompts—e.g., middle-school biology like mitochondria, epidemiology, and biostatistics—rather than only high-risk areas such as CRISPR or pathogen engineering. This is not a benchmark or formal technical report; it is anecdotal evidence of possible safety-policy/classifier routing issues, including one commenter claiming such prompts “switches to opus.” Commenters are strongly negative, arguing the restrictions are excessive and make the model unusable for legitimate science education or biomedical analysis.

    • Users report that Anthropic’s safety/routing behavior may be overblocking benign life-science queries, including basic biology such as mitochondria and non-actionable epidemiology/biostatistics questions. One commenter claims epidemiological or biostatistical prompts trigger a model switch to Claude Opus, suggesting automated classification/routing for bioscience-related content rather than just refusing explicitly actionable wet-lab requests.
    • A technical concern raised is that restrictive frontier-model policies could push researchers toward open-source models as they close the capability gap, especially if proprietary providers block routine scientific analysis or education-level biology.

2. Claude Fable 5 Enterprise Adoption Risks

  • Microsoft is restricting employees from using Claude Fable 5 (Activity: 2044): Microsoft has reportedly restricted internal employee access to Anthropic’s Claude Fable 5 in GitHub Copilot model pickers while legal teams review Anthropic’s Mythos-class retention policy, according to The Verge. The technical blocker is that Fable 5 is not covered by the same Zero Data Retention posture as other Claude models: prompts and outputs are retained for 30 days for safety classifiers, with policy-flagged data potentially retained for up to 2 years, creating risk for confidential/customer data exposure despite Microsoft offering the model to Copilot and Foundry customers. Top commenters largely view the restriction as standard enterprise AI governance: during a retention phase, Fable 5 should only be used in controlled trials or non-sensitive workflows. Several argue this undermines the expected enterprise privacy model—“the whole point of the enterprise plan was so that Anthropic can’t really see your data”—making a blanket internal block the simplest mitigation.

    • Several commenters framed Microsoft’s restriction as standard enterprise governance because Claude Fable 5 reportedly has a mandatory data-retention phase, making it unsuitable for sensitive corporate prompts except in controlled trials with non-sensitive data. The technical concern is not model quality but data-handling: enterprise plans are expected to prevent provider visibility into prompts, while commenters claim Fable 5 changes that assumption.
    • One commenter reported their company also disabled access to the model via AWS Bedrock, despite existing zero-retention agreements. They said internal guidance claimed Fable 5’s 30-day retention requirement effectively bypasses zero-retention policy and contractual expectations that prompts/data would not be inspectable.

  • Claude Fable 5 pricing is $50/Million tokens… are we reaching enterprise-only AI? (Activity: 939): The image is a dark-themed Claude model pricing table showing Claude Fable 5 / Mythos 5 API pricing at $10/Mtok input and $50/Mtok output, with cache writes also $10/Mtok and cache hits $1/Mtok. The post frames this as a concern for indie developers and agent builders, especially since recent Opus 4.x models in the screenshot are lower at $5/Mtok input and $25/Mtok output, while deprecated Opus 4.1 is listed higher at $15/$75 per million input/output tokens. Commenters debate whether this reflects the “actual price of AI” versus unsustainable enterprise-oriented pricing. Several argue local/open-source models may become preferable for high-token workloads, with one commenter citing 50M tokens/day on a local Qwen setup, while another claims Fable is still subsidized and could become more expensive later.

    • One commenter argues that high hosted-model pricing may make local inference economically compelling once open models are “good enough.” They claim to run Qwen 3.6 27B locally at roughly 50M tokens/day on about $4k of hardware, with the expectation that the same hardware will remain useful for open-source model releases over the next 5 years; they estimate open models trail frontier SoTA by only 12–18 months.
    • Another technically relevant pricing comparison notes that Mythos preview was priced around $25/$125 per million tokens, implying Claude Fable 5’s reported $50/M pricing may still be subsidized relative to actual serving costs. The commenter speculates pricing could rise further after an IPO, suggesting current frontier-model APIs may not yet reflect full compute and margin costs.
    • Several commenters expect distillation and competition to pressure prices downward, specifically mentioning Chinese labs distilling expensive frontier models and ongoing competition between ChatGPT and Gemini. The technical implication is that API pricing may bifurcate: expensive frontier models for enterprise/high-value workloads, with cheaper distilled or open-weight models covering “good enough” use cases.

  • The Claude Code active attack didn’t stop. 294,842 secrets stolen from 6,943 machines. It evolved and now spreads through Python too and uses Claude Code itself to steal your secrets. The risk to your credentials just got bigger. (Activity: 1518): OP claims the ongoing UNC6780/TeamPCP / Shai-Hulud–style supply-chain campaign has expanded from npm/VS Code/Claude Code backdooring into Python/PyPI, citing 294,842 secrets stolen from 6,943 machines and 454,648 new malicious packages, mostly npm, from reports such as GitGuardian and Sonatype. The described “Hades” variant allegedly persists via Python startup hooks, fetches Bun to execute JS payloads outside Node-focused detection, uses prompt-injection text to bypass AI package scanners, and modifies config/startup hooks for AI coding tools including Claude, Cursor, Copilot, Gemini, and Codex; sources cited include Socket, Orca Security, Microsoft, and StepSecurity. The attack objective remains credential theft—GitHub/npm/cloud/SSH/API keys—with OP emphasizing that leaked keys can be abused in ~1 minute, many orgs take ~94 days to remediate exposed secrets, and many intrusions are “malware-free” credential logins rather than detectable binaries. The top comments are not technically substantive: they mainly criticize the post length, ask for a TL;DR, or joke about feeding the post back into Claude for summarization.

    • A commenter clarifies the attack scope: it affects developers who installed specific compromised packages, notably bioinformatics PyPI packages such as ensmallen, gpsea, and spateo-release, plus some npm packages. They emphasize it is not self-propagating malware: “It doesn’t spread to machines on its own.”

AI Discords

Unfortunately, Discord shut down our access today. We will not bring it back in this form but we will be shipping the new AINews soon. Thanks for reading to here, it was a good run.