# [AINews] Loopcraft: The Art of Stacking Loops > Source: > Published: 2026-06-12 05:34:09+00:00 # [AINews] Loopcraft: The Art of Stacking Loops ### a quiet day lets us highlight a great concept from Peter Steinberger, Boris Cherny, and Andrej Karpathy There’s a lot of “loop discourse” in the air: [Steipete](https://x.com/steipete/status/2063697162748260627): “Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.”[Boris](https://x.com/0xwhrrari/status/2064804504608887040): “I don’t prompt Claude anymore. I write loops, the loops do the work.”[Andrej](https://www.youtube.com/watch?v=kwSVtQ7dziU)on[Autoresearch](https://www.latent.space/p/ainews-autoresearch-sparks-of-recursive?utm_source=publication-search): To get the most out of the tools that have become available now you have to**remove yourself as the bottleneck**. You can’t be there to prompt the next thing. You need to take yourself outside. You have to** arrange things such that they’re completely autonomous**and the more you know how can you maximize your token throughput and** not be in the loop**. This is the goal and the name of the game now is to** increase your leverage**…. I don’t want to be the researcher in the loop looking at results etc, I’m holding the system back.** So the question is how do I refactor all the abstractions so that I’m not I have to arrange it once and hit go.**” We like this a lot and people don’t realize how many loops we are already in: More minimalist, a smaller set of loops: One might argue the entire game of the next century is to be able to **stack loops** as effectively as possible. In the early days of each phase, it will be valuable to know when to go **DOWN** a loop when things go wrong (for **reliability**)… but it will probably be more valuable to know how to go **UP** a loop as models improve (for **leverage**). If you don’t figure out how to do this, don’t be salty when you lose to those that do. Rich has his “[Bitter Lesson](https://x.com/RichardSSutton/status/2056419165502935198)” for models. We now have **the Salty Lesson for agents**: Don’t fix things yourself, as you have done historically. Instead focus on systems that scale with more agents, like goals and orchestration. 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](https://x.com/simonw/status/2064918665859080392)welcomed the rollback;[MTS live](https://x.com/MTSlive/status/2064922000020398331)summarized that Anthropic was reversing the policy;[Kim Monismus](https://x.com/kimmonismus/status/2065003618710008084)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](https://x.com/code_star/status/2064931207310118940)argued safeguards are normal but “obfuscation without warning” violates the user/provider contract, while[Clement Delangue](https://x.com/ClementDelangue/status/2065069246124613999)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](https://x.com/RyanPGreenblatt/status/2064948033423598035)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](https://x.com/RyanPGreenblatt/status/2065182720133841069),[2](https://x.com/RyanPGreenblatt/status/2065174434672148487)).[Natasha/Lambert](https://x.com/natolambert/status/2065082135682383950)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](https://x.com/GergelyOrosz/status/2065029326215528474)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](https://x.com/htihle/status/2065050640154350043)reported** 87.8% on WeirdML**, the first model above 70% average on each task there.[ProximalHQ](https://x.com/ProximalHQ/status/2065184730279223410)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](https://x.com/threepointone/status/2065131942279016700)spent about**$250** on a ~10k LOC PR and didn’t find it worth it;[Cline](https://x.com/cline/status/2065192415498277335)said cheaper models plus adversarial review loops often match or beat it on cost/perf;[tamaybes](https://x.com/tamaybes/status/2065147305494450248)described Fable inventing internal “codenames” during coding, leaking its own “neuralese” into outputs. Benchmarks also suggested sharp asymmetries depending on task framing:[scaling01](https://x.com/scaling01/status/2065209370145702040)pointed to**200/200 refusals on ProgramBench**, while[thoughtfullab](https://x.com/thoughtfullab/status/2065096885514227876)and[karinanguyen](https://x.com/karinanguyen/status/2065198770292146280)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](https://x.com/RichardSocher/status/2065094362774876232)and[Recursive SI](https://x.com/_rockt/status/2065061990800802249), 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](https://x.com/_rockt/status/2065061993271202171). Detail tweets from[cong_ml](https://x.com/cong_ml/status/2064992941844615246)gave the metrics: on NanoChat, reaching the same loss**1.3× 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](https://x.com/HuggingPapers/status/2065062300218749172)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](https://x.com/thoughtfullab/status/2065096885514227876)positioned** PostTrainBench**as a recursive-self-improvement eval—AI training weaker models and measuring loop progress directly.[dawnsongtweets](https://x.com/dawnsongtweets/status/2065095757988868190)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](https://x.com/manoelribeiro/status/2065055795998233039)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](https://x.com/gui_penedo/status/2064981375694909757),[Hynek Kydlíček](https://x.com/HKydlicek/status/2064984505706774779), and[Macrodata Labs](https://x.com/macrodata_labs/status/2064984775652192652). 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](https://x.com/code_star/status/2064997532602663203),[eliebakouch](https://x.com/eliebakouch/status/2065114511439249852)).**Data quality/debugging is becoming more explicit and instrumented**:[Goodfire](https://x.com/GoodfireAI/status/2065118189986717902)introduced** predictive data debugging**, arguing that preference/DPO datasets contain hidden pathologies—from broken guardrails to hallucinations—and should be analyzed before training.[AllenAI](https://x.com/allen_ai/status/2065100726032839024)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](https://x.com/kamtybor/status/2065028126636204243)proposes an** extract → transform → commit**memory maintenance loop instead of naively appending chat logs;[Weaviate Playground](https://x.com/weaviate_io/status/2065055262851973306)packaged this and related RAG/agent demos. On the retrieval side,[Qdrant](https://x.com/qdrant_engine/status/2065056457461321761)argued larger context windows do**not** make retrieval obsolete because context still imposes cost/latency, while[rishdotblog](https://x.com/rishdotblog/status/2065026144903315545)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](https://x.com/demishassabis/status/2064873362799600042)highlighted** DiffusionGemma**, described as** 4× faster**than other Gemma 4 variants;[osanseviero](https://x.com/osanseviero/status/2065041448135770436)said demos had to be slowed down for viewers.[Unsloth](https://x.com/UnslothAI/status/2065107734916432189)released**Gemma 4 MTP GGUFs**, claiming** 1.4–2.2×**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](https://x.com/baseten/status/2065100012934095171)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](https://x.com/RyanLeeMiniMax/status/2065010795625562486)open-sourced its high-performance** MSA kernel library**, with model weights expected shortly after;[iamgrigorev](https://x.com/iamgrigorev/status/2065074479621935355)pointed to the paper release.[Together](https://x.com/togethercompute/status/2065109302717669392)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](https://x.com/charles_irl/status/2065148183412695282)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](https://x.com/ClaudeDevs/status/2065080005328249086)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](https://x.com/ClaudeDevs/status/2065080009203892302)).[Perplexity](https://x.com/perplexity_ai/status/2065124930463916317)integrated**Deep Research as a native skill inside Computer**, backed by its “search as code” architecture ([details](https://x.com/perplexity_ai/status/2065124948793028691)). 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](https://x.com/Teknium/status/2065060810729414695)unified profile management in** Hermes Agent**, then added remote file access in the desktop app ([remote files](https://x.com/Teknium/status/2065112576552526168)).[Cognition](https://x.com/cognition/status/2065156301668171873)and[imjaredz](https://x.com/imjaredz/status/2065153770762154186)open-sourced**/handoff**, letting local coding agents offload jobs to cloud Devins.[Cursor](https://x.com/cursor_ai/status/2065137803084857845)made**auto-review** the default for new users with a classifier subagent gating actions, claiming**97% accuracy**.[Microsoft](https://x.com/MicrosoftAI/status/2065133021049782491)rolled out** MAI-Code-1-Flash**across Copilot tiers, while[pierceboggan](https://x.com/pierceboggan/status/2065130447630487821)emphasized support for both model and harness choice.[LangChain](https://x.com/LangChain/status/2065090475913068766)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)** **Fable 5 product discourse dominated attention**: the highest-engagement technical-adjacent posts were highly anecdotal but still informative about perception.[aaronli’s claim that Fable 5 “solved CAD”](https://x.com/aaronli/status/2064876123109089742)drew major attention, while[KradleAI’s thread claiming Fable 5 “lies 96% of the time”](https://x.com/kradleai/status/2064907897373642912)captured the opposite pole: high capability mixed with trust concerns.**DiffusionGemma’s speed became a breakout systems story**:[Demis Hassabis’s post](https://x.com/demishassabis/status/2064873362799600042)on** 4× faster**text diffusion for Gemma drove unusually high engagement for an inference/systems topic, suggesting strong appetite for non-autoregressive speedups that actually ship.**AI economics and pricing got broad traction**:[Kim Monismus’s post](https://x.com/kimmonismus/status/2064987311402537184)arguing that premium AI subscriptions are massively subsidized—estimating**$8k equivalent usage for Claude Max 20x** and**$14k for ChatGPT Pro 20x**—was one of the more widely shared technical-business threads, especially alongside reports that[OpenAI may consider token price cuts](https://x.com/kimmonismus/status/2065043333941207160). **AI Reddit Recap** **/r/LocalLlama + /r/localLLM Recap** ## Keep reading with a 7-day free trial Subscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.