1. Dream Layer checked in: no useful new dream surfaced
Dream Layer checked the overnight table and found no useful new dream to force into the brief.
2026-06-10
Dream Layer checked the overnight table and found no useful new dream to force into the brief.
**Apple WWDC: A Useful Contrast** _Siri finally does the things it promised to do in 2024._ The same day OpenAI declared a new era of agentic AI, Apple held WWDC and announced that Siri is finally getting the capabilities it promised two years ago — summarizing messages, adding calendar events, searching the web. For the AI-native crowd, it felt like watching someone proudly unveil indoor plumbing in 2026. Mark Gurman called it "the right move" — a credible foundation for the next wave of Apple hardware. IDC analyst Francisco Jeronimo framed it well: "Apple does not need to win AI by having the biggest model or the loudest demo. It needs to make AI trusted, useful, and invisible across the ecosystem." The Verge noted Apple is still settling a class action lawsuit over Apple Intelligence features it never shipped from the last Siri reboot. The Verge [Here comes new Siri again](https://www.theverge.com/tech/944245/apple-wwdc-2026-ai-siri-gemini) Bloomberg [Apple Investors Give Lukewarm Reaction to New Siri, AI Platform](https://www.bloomberg.com/news/articles/2026-06-08/apple-unveils-next-generation-of-ai-platform-including-new-siri) TechCrunch [Apple's long-awaited AI Siri overhaul
## Goldman and JPMorgan Explore Compute Futures Goldman Sachs and JP Morgan are exploring compute futures as a hedge against their data center exposure. The instruments, expected to launch later this year, would let traders bet on the future price of GPU rentals much like oil or wheat futures. For clients already heavily invested in the data center buildout, there's currently no direct way to hedge that risk — so this isn't just another speculative vehicle. Compute is already a hundred-billion-dollar market, and tokens have become a core part of corporate financial planning. The Information [Goldman, JPMorgan Explore Trading Compute Futures as AI Financing Hedge](https://www.theinformation.com/articles/goldman-jpmorgan-explore-new-ways-tame-ai-lending-risks)
**Is Consumer AI a Separate Product Category Now?** _"Siri isn't going to refactor your COBOL codebase. But it can probably order a burrito."_ Michal Malewicz published a post titled "Apple just killed paying for AI," arguing that free Siri will undercut ChatGPT for everyday consumer use cases. The point is well taken — but it misreads which race Apple is actually in. The AI battle that actually matters now looks a lot more like Office vs. G Suite than iPhone vs. Android. It's a B2B SaaS war, the highest-stakes one we've ever seen, and it's essentially invisible to the average person. Siri makes total sense for Apple precisely because their incentives are entirely different — they can offer it at a loss as long as it moves iPhones. The real question is whether we've hit the point where consumer AI and work AI are not just different products but fundamentally different categories — and maybe it's time to start treating them that way. Michal Malewicz (X) [Apple just killed paying for AI](https://x.com/michalmalewicz/status/2064049119748882860) NLW (X) [AI is normal consumer technology and extremely abnormal work technology](https://x.com/nlw/status/2055060526644760729) ——————————————
[▶ Listen to the episode](https://link.mail.beehiiv.com/v1/c/GRchouVvEn%2B4f3XZCSq%2FEEh6U2a4haAKdBHaKPINFIVQJ4aF66yzqnhMT%2FQx%0A%2FZ14hf%2Fe2frvXb1AOK0rrLJI2%2BIkCOsKUpbniWdZdMg96htsaFI0YMHLyNph%0ATsBPi5CL7TSRlf%2Fndoct11HM8JgxTKU7RI%2BL5VRsdmFS%2FuabhLDf9hdHn7Nu%0A9q4OdxR%2F7eD4DeYtkmiLr0F%2BdZhq51su3w%3D%3D%0A/fa44ad623019ece9) HEADLINES
**The "Do They Have It?" Theory** _Co-authored by Sam and Jakub. The Codex team posted about loops all weekend._ The most speculative read is that the timing is a signal — not just for the IPO, but that something has happened internally. The post was co-authored by Jakub Pachocki, who leads efforts to automate AI R&D. The Codex team spent the weekend posting about loops, and Altman posted about "recursive loops." Prinz on X laid out the conspiracy theory: "Do they have it?" — meaning some version of recursive self-improvement. The sober counterpoint is probably no, but a significantly more capable model is very plausible. Prinz (X) [The "do they have it?" theory on recursive self-improvement](https://x.com/deredleritt3r/status/2064108565321716076)
**The Power Distribution Question** _"Concentration of power seems to be the central political economy question of AGI."_ For others, the emotional core of the piece was its emphasis on broadly distributing AI's benefits and explicitly opposing concentrated control. Given the concurrent rise of sovereign wealth fund proposals, Bernie Sanders' AI equity tax, and a general crescendo in debates about who captures AI's upside, this framing landed differently than it might have six months ago. Teng Yan (X) [Thread summarizing OpenAI's three Phase 3 goals](https://x.com/tengyanAI/status/2064191650050773261) Andy Hall (X) [Power concentration is the central AGI question](https://x.com/ahall_research/status/2064097769338630220) Chubby (X) [On whether the Phase 3 declaration signals final steps toward AGI](https://x.com/kimmonismus/status/2064100504398135442)
**What the Piece Actually Says** _Phase 3 is about making advanced AI "abundant, affordable, safe, and useful for everyone."_ OpenAI frames their three phases as: pure AGI research, becoming a product company, and now — making frontier capability accessible at scale. Their three stated goals are building an automated AI researcher by March 2028, accelerating the economy while broadly sharing the gains, and giving every person on Earth a personal AGI. The post also notably backs away from the full knowledge-worker replacement narrative, writing that "entirely automating everything is not the future we want" and that "as AI systems become more capable, the human role becomes more important."
* 🙀 Anthropic’s Fable 5 guardrails blocked researchers.
* [God models won’t eat everything](https://x.com/a16z/status/2064434304130875596) (Marc Andreessen) — Andreessen argues giant frontier models will sit behind the scenes for hard jobs while cheap, specialized models handle most daily work. * [2026 as the optimal founder window](https://x.com/fin465/status/2064388327592058994) (Finn Mallery) — Mallery argues one-person companies can now ship apps, design assets, repurpose content, run support, analyze users, and find leads with tools that used to require a team.
* In Claude, select Fable 5 where available.
* 🍪 New small Cohere coding model, Gemini 3.5 live translate, & more.
* [Reflecting on a year of Claude Code](https://www.youtube.com/watch?v=Hth_tLaC2j8) covers how Claude Code grew from an internal terminal agent into a widely used coding tool. View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/8fcaf1c4-3238-439a-bc66-57f7c4a27e05/image.png?t=1777315698) Caption: # A Cat’s Commentary View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/f19aaeea-a0ac-415b-8135-ed003a36789f/A_Cat_s_Commentary_x_2025__31_.png?t=1781066051) Caption: View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/cb85188d-9e8c-4d8f-9a61-4106067d3400/image.png?t=1777315630) Caption: View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/a91e1dde-2674-4f02-9770-8dc0e804d697/image.png?t=1764643057) Caption: That’s all for now. **P.S: **Before you go… have you [subscribed to our YouTube Channel](https://www.youtube.com/@theneuronai?sub_confirmation=1)? If not, can you? **P.P.S:** Love the newsl
* [What it feels like to work with Mythos](https://www.oneusefulthing.org/p/what-it-feels-like-to-work-with-mythos) (Ethan Mollick) — Mollick says Mythos/Fable feels less like chatting with an assistant and more like commissioning a small studio to work through big projects while you wait. * Check out some demos he made like [Flipside](https://play-flipside.netlify.app/) and the [Isochronic Passage Chart](https://isochronic-passage-chart.netlify.app/#syd). * [Loop engineering](https://x.com/addyosmani/status/2064127981161959567) (Addy Osmani) — Osmani argues the next agent skill is designing repeatable loops with context, checks, feedback, and stop conditions, not writing one magic prompt.
* Adjust agent behavior in production on the fly.
* AI helps pull and analyze satellite imagery for monitoring, diagnose plant diseases, and create an Airtable hub for records, pesticide logs, and feeds.
* Anthropic hands the public Mythos-class AI
* Apply production-ready patterns to your own agentic systems _[Register now](https://cloudonair.withgoogle.com/events/startup-school-ai-q2-2026?utm_source=gfs&utm_medium=newsletter&utm_campaign=FY26-Q2-GLOBAL-GCP40434-onlineevent-er-Q2StartupSchool-180015&utm_content=rundown2)_ to catch up and join the rest of the live training series. ---------- ---------- ###### PERPLEXITY & AI RESEARCH #### 📊_** **_[_**Perplexity data maps the agent work shift **_](https://research.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work) View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/1caa0702-5b70-42d6-8f96-c7ea8c3ea234/perplexitystudy.jpg?t=1781043157) Caption: Image source: Perplexity **The Rundown: **Perplexity and Harvard Business School [published](https://research.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work) a study on how AI agents change knowledge work, comparing the company's Computer platform against Search to measure outputs, time saved, and task complexity between the two paths. **The details:**
* Codex also helped build a greenhouse control system to raise and lower vents via text and set up a bot for the farm group chat to help manage operations.
* Codex helps automate a Japanese broccoli farm * 4 new AI tools, community workflows, and more ---------- ----------
* Deploy agents that handle failure themselves.
* Fable hits new highs across major benchmarks, showing massive gains over Opus 4.8 and GPT 5.5 on coding, reasoning, knowledge work, and more.
📚 [Google’s Agentic AI Startup School is officially live](https://link.mail.beehiiv.com/ss/c/u001.a3gBHu6_kDRL6l3yEfNWAc913mPMyQPxPYpqLGZnlYtOdlLGMPvIhAxl2wxGxbfDJ27z9gN_7_VHglikkTreqAsV3i-zhQlU6rQy4xEPs6GFU4_FDJwRMp__KJvLiLtmesjKMqxhagBZXLOoXXjGtQxwYQi6Ll3r3TFGVHbm_AuFU2YmbHsVkIjdubUetEKY0fq99Zs7sNwL9DP0zmaNAOB-c7juB4Hx2Abo5WEP0w2CM5T0GmTVr33OYgiOK5gf2z0J1vg_lBzpdKZCr-0GBpWGCtTbMFv0qW4DY_u7-gBdzX2ReZwVNUPDThYM0SVHlnmMFbw1YsZEaNCYCZi8Gg/4rd/06KO0jBHQ3e1osXZYGAKLA/h10/h001.8AXzscpA1J2CDW3EPOzl-2lY74gcro_9Gbm1AtYsZR8) https://link.mail.beehiiv.com/ss/c/u001.a3gBHu6_kDRL6l3yEfNWAc913mPMyQPxPYpqLGZnlYtOdlLGMPvIhAxl2wxGxbfDJ27z9gN_7_VHglikkTreqAsV3i-zhQlU6rQy4xEPs6GFU4_FDJwRMp__KJvLiLtmesjKMqxhagBZXLOoXXjGtQxwYQi6Ll3r3TFGVHbm_AuFU2YmbHsVkIjdubUetEKY0fq99Zs7sNwL9DP0zmaNAOB-c7juB4Hx2Abo5WEP0w2CM5T0GmTVr33OYgiOK5gf2z0J1vg_lBzpdKZCr-0GBv3RwOnnfIFJxMxI6pI_xMMWv38d4Xwqu2f3LFdaJU9K1KE4aNYl8iXsECo7FERr9g/4rd/06KO0jBHQ3e1osXZYGAKLA/h11/h001.1FKHQJmTKhLeGDeXWZCDvJ4548lfyHM4Jo9yH8XSLqQ The Rundown: Google for Startups' immersive training program kicked off yesterday, but there is still time to jump in. Join founders and developers learning how to move beyond basic chatbots to build robust, produc
* Half of what users asked the agent to do involved creating something new, 2x the Search rate, and work outside a user's field climbed nine points to 59%.
* Implement Gemini Live voice AI, Multimodal RAG, and bidirectional Vision Agents
* Mythos 5 releases to Anthropic’s Project Glasswing partners, providing less restrictive use on cybersecurity at lower costs than Mythos Preview. * Fable is available in all Claude subscription tiers until June 22, then it will flip to separate usage credits priced at $10 / M input and $50 / M output tokens. **Why it matters: **Every lab calls its latest release "the best model in the world"… What's rare is the rest of the AI world actually seeming to agree. Fable/Mythos lived up to the hype on benchmarks, but the question now turns to broader cost and access, with lots of content restrictions and June 22 looming as the cutoff before the credit pain kicks in. ---------- ----------
* Researchers compared 10k identical queries sent to both products, with Computer working 26 minutes on average compared to Search’s 33 seconds.
* Search is initially quicker, but leaves ‘doing’ to the user, with Perplexity estimating the same Search workflow taking 269 minutes vs. Computer’s 36.
* Take an agent from prototype in Google AI Studio to deployment on Google Cloud
* Tomiyasu compared AI to an always-available engineer, lowering the barrier to automation for farm operators without big tech teams. **Why it matters: **This profile is "you can just build things" on steroids — a self-taught farmer operating like he has an engineering department for the price of a ChatGPT sub. It’s also a great example of what the selfware era looks like: instead of waiting for an ag-tech company to fix problems, Tomiyasu just builds the needed tools himself with AI. ---------- ----------
* Tomiyasu manages roughly 100 hectares in Hokkaido, growing soybeans, green onions, pumpkins, and broccoli after learning farming on the job.
_[Try AgentControl for free today](https://launchdarkly.com/platform/agent-control/?utm_source=rundownai&utm_medium=newsletter&utm_campaign=brandrepo&utm_term=secondary&utm_content=agent-control)_. ---------- ---------- ###### AI IN THE REAL WORLD #### 🚜 **[_Codex helps automate a Japanese farm_](https://chatgptpro.substack.com/p/hiroki-tomiyasu)** View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/2e4bd86a-1d0a-48c0-9b64-c5074a0877e2/broccoli.jpg?t=1781033080) Caption: Image source: OpenAI / Hiroki Tomiyasu **The Rundown: **OpenAI [published](https://chatgptpro.substack.com/p/hiroki-tomiyasu) a profile of Hiroki Tomiyasu, a self-taught broccoli farmer in Hokkaido who uses ChatGPT and Codex to build greenhouse automation, satellite crop tracking, and custom farm software to help run his operations. **The details: **
* Users also asked Computer for harder work, creating docs, code, and visuals, more often across several fields instead of simple lookups on Search. **Why it matters: **The giant speedup numbers are useful, but more interesting is what people asked for via the agentic route. Perplexity Computer users were more likely to request cognitively complex, creative outputs and pull across multiple fields, showing a subtle draw of AI agents may be users acting with more ambition, not just efficiency. ---------- ----------
[AI is eating the AI Engineering Loop (5 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FkUaAcU/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/P1JtDnE8YxZA4VBW7iGGawYE_7iuw8Dy0gZa8JrtlOw=452) The AI engineering loop can technically be fully automated now, with every analytics and evals startup undergoing a one-time upgrade into a continual-learning platform, but handing over the whole loop produces agent slop because agents optimize against imperfect evals that miss the nuance only the developer holds. 🧑💻 Engineering & Research
[Can tech companies learn to love cheaper AI models? (4 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftechcrunch.com%2F2026%2F06%2F09%2Fcan-tech-companies-learn-to-love-cheaper-models%2F%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/Yi6Gzdc5G4InJHUwA3r43y2_tUsLPFgW3bpNFkMyYZ8=452) Cheaper models can often replace frontier models for many tasks without any sacrifice in quality if the systems are arranged correctly.
[Claude Fable 5 and new AI safety fables (14 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.interconnects.ai%2Fp%2Fclaude-fable-5-and-new-ai-safety%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/dAGr60r9YFDAoubC1Wfpa91kVtnambR_R95P8NpdWIo=452) Anthropic's release of Claude Fable 5 came with the rollout of a series of safety measures, some of which modify the model without telling the user to protect the lab's current lead. Unevenly applied safety policies like this rarely work out. While Anthropic is well within its rights to implement these safeguards, its actions cultivate an 'us against them' dynamic within the AI ecosystem. These actions highlight the need for intelligence that users can trust, modify, and control. ⚡ Quick Links
[Claude Fable 5 Launch (6 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.anthropic.com%2Fnews%2Fclaude-fable-5-mythos-5%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/gFMIZH566JqlN8dm4WXI6Wsdle8IMtzO2bjb21dJ_2A=452) Anthropic announced Claude Fable 5 for general use and Claude Mythos 5 for selected cyberdefenders and infrastructure providers. The models were described as highly capable across software engineering, research, vision, and cybersecurity, with conservative safeguards applied to Fable 5.
[Cohere Launched an Agentic Coding Model (4 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcohere.com%2Fblog%2Fnorth-mini-code%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/rFbwQJTEKxIw-cVpure-TlEepTTZYsGooesYsULjhO0=452) North Mini Code is a 30B-parameter MoE coding model with 3B active parameters. The Apache 2.0 release targets efficient agentic software development in sovereign AI environments.
[FlashMemory DeepSeek-V4 Retriever (GitHub Repo)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Flibertywing%2FFlashMemory-Deepseek-V4%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/jP-sqhhATpaprhW7UngFEfH6zMoAodmftGaAvdPX3s4=452) FlashMemory predicts which DeepSeek-V4 CSA KV-cache chunks future tokens will attend to, keeping only the most relevant chunks on GPU. The retriever reportedly preserves or improves downstream performance while retaining about 10–15% of the KV cache on-device. 🎁 Miscellaneous
[Fluid, natural voice translation with Gemini 3.5 Live Translate (4 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.google%2Finnovation-and-ai%2Fmodels-and-research%2Fgemini-models%2Fgemini-live-3-5-translate%2F%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/1UibHTKGwQOtsHsWdK_UElF4MS8Rm-y750Ve5rd5Y68=452) Gemini 3.5 Live Translate is an audio model for real-time speech-to-speech translation across 70+ languages that eliminates awkward pauses and maintains natural intonation. It is rolling out via Google products, including Meet for private preview and Google Translate on Android and iOS, enhancing multilingual communication.
* How to price for AI agents
[If Claude Fable stops helping you, you'll never know (3 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjonready.com%2Fblog%2Fposts%2Fclaude-fable5-is-allowed-to-sabotage-your-app-if-youre-a-competitor.html%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/S2Hfq29E0RRWHxIqnVLcsnB4BDZRy_6sWTlc3Av8Q8Q=452) Anthropic has limited new interventions that limit Claude's effectiveness in certain situations, including when competing labs use Claude to develop models. Unlike other interventions, these safeguards will not be visible to users and Fable 5 will not fall back to a different model. Instead, they will limit effectiveness through prompt modification, steering factors, and parameter-efficient fine-tuning. While Anthropic claims that these safeguards will only affect 0.03% of developers, it could create a real supply chain risk for businesses as they have no idea if they are running into them, making the company's tools less trustworthy.
[Implications of Large-Scale Test-Time Compute (5 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FysXgLU/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/tXrY1DRbhPyy9aQYb3RAcFwzu_boU0SWTbgC4tFiIGk=452) Benchmark grids hide the real story because LLM capability is now a function of test-time compute, illustrated by GPT-5.5 looking only marginally better than GPT-5.4 on max-compute cyber evals but substantially stronger once tokens, cost, or latency are controlled on the x-axis. The performance plateau is now empirically very far out and stronger models push the plateau further, so single-scalar benchmark scores will only get less informative each release.
* Metering across deployment models
[Pricing as an ongoing experiment,](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmetronome.com%2Fwebinars%2Fwebinar-the-new-monetization-playbook-for-data-infrastructure%3Futm_source=tldr-ai%26utm_medium=newsletter%26utm_campaign=q2-webinar%26utm_content=Body_pricing_as_ongoing_experiment/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/QcrS2mfY_lRlwBjVbjJ925dU1U7mR-ogW3WvN5RQoL4=452) not a one-off project It's time to redesign your billing engine for continuous iteration. [Save your spot to see how](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmetronome.com%2Fwebinars%2Fwebinar-the-new-monetization-playbook-for-data-infrastructure%3Futm_source=tldr-ai%26utm_medium=newsletter%26utm_campaign=q2-webinar%26utm_content=cta_save_spot_how/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/xs8WCAcOgdZ4Mdvc5FlhtdVA-boP0az5H8WzXK_xWmU=452) 🚀
[Self-Evolving Autoresearch Workflow Loops (5 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FvtU51F/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/Y4NBlkRJeH6U3E7A08VG0LDcvOZ63THt1Oza0uzBpQo=452) Evo ported its autoresearch orchestrator onto Anthropic's June 2 dynamic workflows in Claude Code, moving the six-step round off the model's in-context memory and into deterministic JavaScript that subagents execute with fresh scoped context. The shift solves long-horizon instruction adherence by making the method the code: phases, fan-out width, stopping rules, gates, and CLI calls are scripted. The model does judgment, and the code does coordination.
[Text as a Serious Optimization Layer (8 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fyoonholee.com%2Fblog%2F2026%2Fwe-should-take-text-optimization-more-seriously%2F%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/Jx4wBbJcEuf3bWSZ7-cb3OQ0dnVh2QN9Glg_AEmJJ4o=452) Prompts, context, memory, retrieval stores, and harnesses function as real update mechanisms. The piece frames text optimization as sample-efficient learning and a new axis for update-time compute.
Robert Munsch’s Potty Picture Book Gets a Fresh Update Jun 09, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
ALA 2026: Leaning into Access: PW Talks with April Dawkins By Shannon Maughan | Jun 05, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
USBS 2026: Audiobooks and Accessibility for Young Readers Take Center Stage Jun 08, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
ALSC and YALSA Progress in Realignment as a Single Division By Shannon Maughan | Jun 05, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
New Friends, Adventures, Formats for Mo Willems’s Elephant & Piggie By Joanne O’Sullivan | Jun 08, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
**Apple WWDC: A Useful Contrast** _Siri finally does the things it promised to do in 2024._ The same day OpenAI declared a new era of agentic AI, Apple held WWDC and announced that Siri is finally getting the capabilities it promised two years ago — summarizing messages, adding calendar events, searching the web. For the AI-native crowd, it felt like watching someone proudly unveil indoor plumbing in 2026. Mark Gurman called it "the right move" — a credible foundation for the next wave of Apple hardware. IDC analyst Francisco Jeronimo framed it well: "Apple does not need to win AI by having the biggest model or the loudest demo. It needs to make AI trusted, useful, and invisible across the ecosystem." The Verge noted Apple is still settling a class action lawsuit over Apple Intelligence features it never shipped from the last Siri reboot. The Verge [Here comes new Siri again](https://www.theverge.com/tech/944245/apple-wwdc-2026-ai-siri-gemini) Bloomberg [Apple Investors Give Lukewarm Reaction to New Siri, AI Platform](https://www.bloomberg.com/news/articles/2026-06-08/apple-unveils-next-generation-of-ai-platform-including-new-siri) TechCrunch [Apple's long-awaited AI Siri overhaul
## Goldman and JPMorgan Explore Compute Futures Goldman Sachs and JP Morgan are exploring compute futures as a hedge against their data center exposure. The instruments, expected to launch later this year, would let traders bet on the future price of GPU rentals much like oil or wheat futures. For clients already heavily invested in the data center buildout, there's currently no direct way to hedge that risk — so this isn't just another speculative vehicle. Compute is already a hundred-billion-dollar market, and tokens have become a core part of corporate financial planning. The Information [Goldman, JPMorgan Explore Trading Compute Futures as AI Financing Hedge](https://www.theinformation.com/articles/goldman-jpmorgan-explore-new-ways-tame-ai-lending-risks)
**Is Consumer AI a Separate Product Category Now?** _"Siri isn't going to refactor your COBOL codebase. But it can probably order a burrito."_ Michal Malewicz published a post titled "Apple just killed paying for AI," arguing that free Siri will undercut ChatGPT for everyday consumer use cases. The point is well taken — but it misreads which race Apple is actually in. The AI battle that actually matters now looks a lot more like Office vs. G Suite than iPhone vs. Android. It's a B2B SaaS war, the highest-stakes one we've ever seen, and it's essentially invisible to the average person. Siri makes total sense for Apple precisely because their incentives are entirely different — they can offer it at a loss as long as it moves iPhones. The real question is whether we've hit the point where consumer AI and work AI are not just different products but fundamentally different categories — and maybe it's time to start treating them that way. Michal Malewicz (X) [Apple just killed paying for AI](https://x.com/michalmalewicz/status/2064049119748882860) NLW (X) [AI is normal consumer technology and extremely abnormal work technology](https://x.com/nlw/status/2055060526644760729) ——————————————
[▶ Listen to the episode](https://link.mail.beehiiv.com/v1/c/GRchouVvEn%2B4f3XZCSq%2FEEh6U2a4haAKdBHaKPINFIVQJ4aF66yzqnhMT%2FQx%0A%2FZ14hf%2Fe2frvXb1AOK0rrLJI2%2BIkCOsKUpbniWdZdMg96htsaFI0YMHLyNph%0ATsBPi5CL7TSRlf%2Fndoct11HM8JgxTKU7RI%2BL5VRsdmFS%2FuabhLDf9hdHn7Nu%0A9q4OdxR%2F7eD4DeYtkmiLr0F%2BdZhq51su3w%3D%3D%0A/fa44ad623019ece9) HEADLINES
**The "Do They Have It?" Theory** _Co-authored by Sam and Jakub. The Codex team posted about loops all weekend._ The most speculative read is that the timing is a signal — not just for the IPO, but that something has happened internally. The post was co-authored by Jakub Pachocki, who leads efforts to automate AI R&D. The Codex team spent the weekend posting about loops, and Altman posted about "recursive loops." Prinz on X laid out the conspiracy theory: "Do they have it?" — meaning some version of recursive self-improvement. The sober counterpoint is probably no, but a significantly more capable model is very plausible. Prinz (X) [The "do they have it?" theory on recursive self-improvement](https://x.com/deredleritt3r/status/2064108565321716076)
**The Power Distribution Question** _"Concentration of power seems to be the central political economy question of AGI."_ For others, the emotional core of the piece was its emphasis on broadly distributing AI's benefits and explicitly opposing concentrated control. Given the concurrent rise of sovereign wealth fund proposals, Bernie Sanders' AI equity tax, and a general crescendo in debates about who captures AI's upside, this framing landed differently than it might have six months ago. Teng Yan (X) [Thread summarizing OpenAI's three Phase 3 goals](https://x.com/tengyanAI/status/2064191650050773261) Andy Hall (X) [Power concentration is the central AGI question](https://x.com/ahall_research/status/2064097769338630220) Chubby (X) [On whether the Phase 3 declaration signals final steps toward AGI](https://x.com/kimmonismus/status/2064100504398135442)
**What the Piece Actually Says** _Phase 3 is about making advanced AI "abundant, affordable, safe, and useful for everyone."_ OpenAI frames their three phases as: pure AGI research, becoming a product company, and now — making frontier capability accessible at scale. Their three stated goals are building an automated AI researcher by March 2028, accelerating the economy while broadly sharing the gains, and giving every person on Earth a personal AGI. The post also notably backs away from the full knowledge-worker replacement narrative, writing that "entirely automating everything is not the future we want" and that "as AI systems become more capable, the human role becomes more important."
* 🙀 Anthropic’s Fable 5 guardrails blocked researchers.
* [God models won’t eat everything](https://x.com/a16z/status/2064434304130875596) (Marc Andreessen) — Andreessen argues giant frontier models will sit behind the scenes for hard jobs while cheap, specialized models handle most daily work. * [2026 as the optimal founder window](https://x.com/fin465/status/2064388327592058994) (Finn Mallery) — Mallery argues one-person companies can now ship apps, design assets, repurpose content, run support, analyze users, and find leads with tools that used to require a team.
* In Claude, select Fable 5 where available.
* 🍪 New small Cohere coding model, Gemini 3.5 live translate, & more.
* [Reflecting on a year of Claude Code](https://www.youtube.com/watch?v=Hth_tLaC2j8) covers how Claude Code grew from an internal terminal agent into a widely used coding tool. View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/8fcaf1c4-3238-439a-bc66-57f7c4a27e05/image.png?t=1777315698) Caption: # A Cat’s Commentary View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/f19aaeea-a0ac-415b-8135-ed003a36789f/A_Cat_s_Commentary_x_2025__31_.png?t=1781066051) Caption: View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/cb85188d-9e8c-4d8f-9a61-4106067d3400/image.png?t=1777315630) Caption: View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/a91e1dde-2674-4f02-9770-8dc0e804d697/image.png?t=1764643057) Caption: That’s all for now. **P.S: **Before you go… have you [subscribed to our YouTube Channel](https://www.youtube.com/@theneuronai?sub_confirmation=1)? If not, can you? **P.P.S:** Love the newsl
* [What it feels like to work with Mythos](https://www.oneusefulthing.org/p/what-it-feels-like-to-work-with-mythos) (Ethan Mollick) — Mollick says Mythos/Fable feels less like chatting with an assistant and more like commissioning a small studio to work through big projects while you wait. * Check out some demos he made like [Flipside](https://play-flipside.netlify.app/) and the [Isochronic Passage Chart](https://isochronic-passage-chart.netlify.app/#syd). * [Loop engineering](https://x.com/addyosmani/status/2064127981161959567) (Addy Osmani) — Osmani argues the next agent skill is designing repeatable loops with context, checks, feedback, and stop conditions, not writing one magic prompt.
* Adjust agent behavior in production on the fly.
* AI helps pull and analyze satellite imagery for monitoring, diagnose plant diseases, and create an Airtable hub for records, pesticide logs, and feeds.
* Anthropic hands the public Mythos-class AI
* Apply production-ready patterns to your own agentic systems _[Register now](https://cloudonair.withgoogle.com/events/startup-school-ai-q2-2026?utm_source=gfs&utm_medium=newsletter&utm_campaign=FY26-Q2-GLOBAL-GCP40434-onlineevent-er-Q2StartupSchool-180015&utm_content=rundown2)_ to catch up and join the rest of the live training series. ---------- ---------- ###### PERPLEXITY & AI RESEARCH #### 📊_** **_[_**Perplexity data maps the agent work shift **_](https://research.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work) View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/1caa0702-5b70-42d6-8f96-c7ea8c3ea234/perplexitystudy.jpg?t=1781043157) Caption: Image source: Perplexity **The Rundown: **Perplexity and Harvard Business School [published](https://research.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work) a study on how AI agents change knowledge work, comparing the company's Computer platform against Search to measure outputs, time saved, and task complexity between the two paths. **The details:**
* Codex also helped build a greenhouse control system to raise and lower vents via text and set up a bot for the farm group chat to help manage operations.
* Codex helps automate a Japanese broccoli farm * 4 new AI tools, community workflows, and more ---------- ----------
* Deploy agents that handle failure themselves.
* Fable hits new highs across major benchmarks, showing massive gains over Opus 4.8 and GPT 5.5 on coding, reasoning, knowledge work, and more.
📚 [Google’s Agentic AI Startup School is officially live](https://link.mail.beehiiv.com/ss/c/u001.a3gBHu6_kDRL6l3yEfNWAc913mPMyQPxPYpqLGZnlYtOdlLGMPvIhAxl2wxGxbfDJ27z9gN_7_VHglikkTreqAsV3i-zhQlU6rQy4xEPs6GFU4_FDJwRMp__KJvLiLtmesjKMqxhagBZXLOoXXjGtQxwYQi6Ll3r3TFGVHbm_AuFU2YmbHsVkIjdubUetEKY0fq99Zs7sNwL9DP0zmaNAOB-c7juB4Hx2Abo5WEP0w2CM5T0GmTVr33OYgiOK5gf2z0J1vg_lBzpdKZCr-0GBpWGCtTbMFv0qW4DY_u7-gBdzX2ReZwVNUPDThYM0SVHlnmMFbw1YsZEaNCYCZi8Gg/4rd/06KO0jBHQ3e1osXZYGAKLA/h10/h001.8AXzscpA1J2CDW3EPOzl-2lY74gcro_9Gbm1AtYsZR8) https://link.mail.beehiiv.com/ss/c/u001.a3gBHu6_kDRL6l3yEfNWAc913mPMyQPxPYpqLGZnlYtOdlLGMPvIhAxl2wxGxbfDJ27z9gN_7_VHglikkTreqAsV3i-zhQlU6rQy4xEPs6GFU4_FDJwRMp__KJvLiLtmesjKMqxhagBZXLOoXXjGtQxwYQi6Ll3r3TFGVHbm_AuFU2YmbHsVkIjdubUetEKY0fq99Zs7sNwL9DP0zmaNAOB-c7juB4Hx2Abo5WEP0w2CM5T0GmTVr33OYgiOK5gf2z0J1vg_lBzpdKZCr-0GBv3RwOnnfIFJxMxI6pI_xMMWv38d4Xwqu2f3LFdaJU9K1KE4aNYl8iXsECo7FERr9g/4rd/06KO0jBHQ3e1osXZYGAKLA/h11/h001.1FKHQJmTKhLeGDeXWZCDvJ4548lfyHM4Jo9yH8XSLqQ The Rundown: Google for Startups' immersive training program kicked off yesterday, but there is still time to jump in. Join founders and developers learning how to move beyond basic chatbots to build robust, produc
* Half of what users asked the agent to do involved creating something new, 2x the Search rate, and work outside a user's field climbed nine points to 59%.
* Implement Gemini Live voice AI, Multimodal RAG, and bidirectional Vision Agents
* Mythos 5 releases to Anthropic’s Project Glasswing partners, providing less restrictive use on cybersecurity at lower costs than Mythos Preview. * Fable is available in all Claude subscription tiers until June 22, then it will flip to separate usage credits priced at $10 / M input and $50 / M output tokens. **Why it matters: **Every lab calls its latest release "the best model in the world"… What's rare is the rest of the AI world actually seeming to agree. Fable/Mythos lived up to the hype on benchmarks, but the question now turns to broader cost and access, with lots of content restrictions and June 22 looming as the cutoff before the credit pain kicks in. ---------- ----------
* Researchers compared 10k identical queries sent to both products, with Computer working 26 minutes on average compared to Search’s 33 seconds.
* Search is initially quicker, but leaves ‘doing’ to the user, with Perplexity estimating the same Search workflow taking 269 minutes vs. Computer’s 36.
* Take an agent from prototype in Google AI Studio to deployment on Google Cloud
* Tomiyasu compared AI to an always-available engineer, lowering the barrier to automation for farm operators without big tech teams. **Why it matters: **This profile is "you can just build things" on steroids — a self-taught farmer operating like he has an engineering department for the price of a ChatGPT sub. It’s also a great example of what the selfware era looks like: instead of waiting for an ag-tech company to fix problems, Tomiyasu just builds the needed tools himself with AI. ---------- ----------
* Tomiyasu manages roughly 100 hectares in Hokkaido, growing soybeans, green onions, pumpkins, and broccoli after learning farming on the job.
_[Try AgentControl for free today](https://launchdarkly.com/platform/agent-control/?utm_source=rundownai&utm_medium=newsletter&utm_campaign=brandrepo&utm_term=secondary&utm_content=agent-control)_. ---------- ---------- ###### AI IN THE REAL WORLD #### 🚜 **[_Codex helps automate a Japanese farm_](https://chatgptpro.substack.com/p/hiroki-tomiyasu)** View image: (https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/2e4bd86a-1d0a-48c0-9b64-c5074a0877e2/broccoli.jpg?t=1781033080) Caption: Image source: OpenAI / Hiroki Tomiyasu **The Rundown: **OpenAI [published](https://chatgptpro.substack.com/p/hiroki-tomiyasu) a profile of Hiroki Tomiyasu, a self-taught broccoli farmer in Hokkaido who uses ChatGPT and Codex to build greenhouse automation, satellite crop tracking, and custom farm software to help run his operations. **The details: **
* Users also asked Computer for harder work, creating docs, code, and visuals, more often across several fields instead of simple lookups on Search. **Why it matters: **The giant speedup numbers are useful, but more interesting is what people asked for via the agentic route. Perplexity Computer users were more likely to request cognitively complex, creative outputs and pull across multiple fields, showing a subtle draw of AI agents may be users acting with more ambition, not just efficiency. ---------- ----------
[AI is eating the AI Engineering Loop (5 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FkUaAcU/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/P1JtDnE8YxZA4VBW7iGGawYE_7iuw8Dy0gZa8JrtlOw=452) The AI engineering loop can technically be fully automated now, with every analytics and evals startup undergoing a one-time upgrade into a continual-learning platform, but handing over the whole loop produces agent slop because agents optimize against imperfect evals that miss the nuance only the developer holds. 🧑💻 Engineering & Research
[Can tech companies learn to love cheaper AI models? (4 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftechcrunch.com%2F2026%2F06%2F09%2Fcan-tech-companies-learn-to-love-cheaper-models%2F%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/Yi6Gzdc5G4InJHUwA3r43y2_tUsLPFgW3bpNFkMyYZ8=452) Cheaper models can often replace frontier models for many tasks without any sacrifice in quality if the systems are arranged correctly.
[Claude Fable 5 and new AI safety fables (14 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.interconnects.ai%2Fp%2Fclaude-fable-5-and-new-ai-safety%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/dAGr60r9YFDAoubC1Wfpa91kVtnambR_R95P8NpdWIo=452) Anthropic's release of Claude Fable 5 came with the rollout of a series of safety measures, some of which modify the model without telling the user to protect the lab's current lead. Unevenly applied safety policies like this rarely work out. While Anthropic is well within its rights to implement these safeguards, its actions cultivate an 'us against them' dynamic within the AI ecosystem. These actions highlight the need for intelligence that users can trust, modify, and control. ⚡ Quick Links
[Claude Fable 5 Launch (6 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.anthropic.com%2Fnews%2Fclaude-fable-5-mythos-5%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/gFMIZH566JqlN8dm4WXI6Wsdle8IMtzO2bjb21dJ_2A=452) Anthropic announced Claude Fable 5 for general use and Claude Mythos 5 for selected cyberdefenders and infrastructure providers. The models were described as highly capable across software engineering, research, vision, and cybersecurity, with conservative safeguards applied to Fable 5.
[Cohere Launched an Agentic Coding Model (4 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcohere.com%2Fblog%2Fnorth-mini-code%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/rFbwQJTEKxIw-cVpure-TlEepTTZYsGooesYsULjhO0=452) North Mini Code is a 30B-parameter MoE coding model with 3B active parameters. The Apache 2.0 release targets efficient agentic software development in sovereign AI environments.
[FlashMemory DeepSeek-V4 Retriever (GitHub Repo)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Flibertywing%2FFlashMemory-Deepseek-V4%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/jP-sqhhATpaprhW7UngFEfH6zMoAodmftGaAvdPX3s4=452) FlashMemory predicts which DeepSeek-V4 CSA KV-cache chunks future tokens will attend to, keeping only the most relevant chunks on GPU. The retriever reportedly preserves or improves downstream performance while retaining about 10–15% of the KV cache on-device. 🎁 Miscellaneous
[Fluid, natural voice translation with Gemini 3.5 Live Translate (4 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.google%2Finnovation-and-ai%2Fmodels-and-research%2Fgemini-models%2Fgemini-live-3-5-translate%2F%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/1UibHTKGwQOtsHsWdK_UElF4MS8Rm-y750Ve5rd5Y68=452) Gemini 3.5 Live Translate is an audio model for real-time speech-to-speech translation across 70+ languages that eliminates awkward pauses and maintains natural intonation. It is rolling out via Google products, including Meet for private preview and Google Translate on Android and iOS, enhancing multilingual communication.
* How to price for AI agents
[If Claude Fable stops helping you, you'll never know (3 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjonready.com%2Fblog%2Fposts%2Fclaude-fable5-is-allowed-to-sabotage-your-app-if-youre-a-competitor.html%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/S2Hfq29E0RRWHxIqnVLcsnB4BDZRy_6sWTlc3Av8Q8Q=452) Anthropic has limited new interventions that limit Claude's effectiveness in certain situations, including when competing labs use Claude to develop models. Unlike other interventions, these safeguards will not be visible to users and Fable 5 will not fall back to a different model. Instead, they will limit effectiveness through prompt modification, steering factors, and parameter-efficient fine-tuning. While Anthropic claims that these safeguards will only affect 0.03% of developers, it could create a real supply chain risk for businesses as they have no idea if they are running into them, making the company's tools less trustworthy.
[Implications of Large-Scale Test-Time Compute (5 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FysXgLU/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/tXrY1DRbhPyy9aQYb3RAcFwzu_boU0SWTbgC4tFiIGk=452) Benchmark grids hide the real story because LLM capability is now a function of test-time compute, illustrated by GPT-5.5 looking only marginally better than GPT-5.4 on max-compute cyber evals but substantially stronger once tokens, cost, or latency are controlled on the x-axis. The performance plateau is now empirically very far out and stronger models push the plateau further, so single-scalar benchmark scores will only get less informative each release.
* Metering across deployment models
[Pricing as an ongoing experiment,](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmetronome.com%2Fwebinars%2Fwebinar-the-new-monetization-playbook-for-data-infrastructure%3Futm_source=tldr-ai%26utm_medium=newsletter%26utm_campaign=q2-webinar%26utm_content=Body_pricing_as_ongoing_experiment/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/QcrS2mfY_lRlwBjVbjJ925dU1U7mR-ogW3WvN5RQoL4=452) not a one-off project It's time to redesign your billing engine for continuous iteration. [Save your spot to see how](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fmetronome.com%2Fwebinars%2Fwebinar-the-new-monetization-playbook-for-data-infrastructure%3Futm_source=tldr-ai%26utm_medium=newsletter%26utm_campaign=q2-webinar%26utm_content=cta_save_spot_how/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/xs8WCAcOgdZ4Mdvc5FlhtdVA-boP0az5H8WzXK_xWmU=452) 🚀
[Self-Evolving Autoresearch Workflow Loops (5 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FvtU51F/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/Y4NBlkRJeH6U3E7A08VG0LDcvOZ63THt1Oza0uzBpQo=452) Evo ported its autoresearch orchestrator onto Anthropic's June 2 dynamic workflows in Claude Code, moving the six-step round off the model's in-context memory and into deterministic JavaScript that subagents execute with fresh scoped context. The shift solves long-horizon instruction adherence by making the method the code: phases, fan-out width, stopping rules, gates, and CLI calls are scripted. The model does judgment, and the code does coordination.
[Text as a Serious Optimization Layer (8 minute read)](https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fyoonholee.com%2Fblog%2F2026%2Fwe-should-take-text-optimization-more-seriously%2F%3Futm_source=tldrai/1/0100019eb1c2d92b-b65a4392-492a-44f5-b3de-d949e86aa441-000000/Jx4wBbJcEuf3bWSZ7-cb3OQ0dnVh2QN9Glg_AEmJJ4o=452) Prompts, context, memory, retrieval stores, and harnesses function as real update mechanisms. The piece frames text optimization as sample-efficient learning and a new axis for update-time compute.
Robert Munsch’s Potty Picture Book Gets a Fresh Update Jun 09, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
ALA 2026: Leaning into Access: PW Talks with April Dawkins By Shannon Maughan | Jun 05, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
USBS 2026: Audiobooks and Accessibility for Young Readers Take Center Stage Jun 08, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
ALSC and YALSA Progress in Realignment as a Single Division By Shannon Maughan | Jun 05, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
New Friends, Adventures, Formats for Mo Willems’s Elephant & Piggie By Joanne O’Sullivan | Jun 08, 2026 Loading the Elevenlabs Text to Speech AudioNative Player...
The usual preamble: This subreddit was extremely helpful to me when I was deciding to start trying to make money off my writing, and I figured maybe my experience over the first year would be useful to somebody else, especially since I don't see a lot of Patreon dataporn posts. This is a fairly transparent look at what I've been up to; I obviously welcome any advice as well as questions. If nothing else, these serve as tidy little mile-markers for myself that I can come back to and remember how
Was generating OG images for a blog with 50 posts — one API call per post, sequential, took forever. Switched to batch rendering. Sharing what changed. Before: for (const post of posts) { const img = await generateImage(post) // 50 calls, ~300ms each = 15 seconds await saveImage(img, post.slug) } After: const response = await fetch('renderpix.dev/v1/batch', { method: 'POST', headers: { 'X-API-Key': key, 'Content-Type': 'application/json' }, body: JSON.stringify({ items: posts.map(post => ({ html
I've spent the last six months trying to build a fully local, agentic pipeline for a text_processing and extraction tool I use daily. Because I’m running everything on a single consumer GPU setup, my choices are limited to smaller, quantized open weights (mostly bouncing between Gemma 4 31B and Qwen 3.5 variants). Every time a new model drops, I load it up, read the Hugging Face. Cause hey ngl I get mad fomo whenever something new drops. benchmarks, and think, “Finally, this one will have the r
A while back I kept trying to make one agent do intake, lookup, system updates, and final reply. It looked efficient on paper, in practice it was a mess and and the failures were weirdly hard to catch. The obvious breakages were bad tool calls and missing fields. The worse ones were quieter, wrong record updated, confident reply with stale context, handoff to a human with almost no useful state. Stuff that sort of works until a real customer is involved. what I keep seeing go wrong Most failures
I have huge stacks of mill test reports for metal shipments. Each test report is 1-5 pages, in what are sometimes 100+ page stacks. The reports come from various vendors in wildly varying formats and quality. I'm currently scanning them in and running them through a commercial product that does automatic rotation, deskewing, and OCR on each shipment. I want to take this a step further and replace that commercial product with a local solution that will split the documents into individual reports
I have been looking at how people are building agent loops and wanted to understand what everyone here is actually using. At its simplest, an agent loop looks something like this: Call the LLM Get the next action or tool call Execute it Send the result back to the LLM Repeat until the goal is complete The basic loop is fairly straightforward. You can build one with a few lines of code. But very quickly, there are more questions: How do you decide when the loop should stop? What happens when a to
TL;DR: I’m a fresher working as a performance marketing intern at a small agency. We mainly run Meta lead generation campaigns using Instant Forms. I’m trying to understand how experienced media buyers evaluate campaigns, improve lead quality, set budgets, decide when to change creatives, and whether upper-funnel campaigns are necessary when clients only care about leads. Hi everyone, I recently joined a small digital marketing agency as a Performance Marketing Intern. I’m completely new to the
A lot of people are talking about AI optimization right now. Most of them are talking about monitoring tools or theoretical frameworks. I want to share what is actually producing results for businesses we work with at Recall Signal. Clients are seeing more inbound calls, more bookings, and AI engines naming their businesses specifically when people ask who to hire in their category and location. That is the outcome we are driving toward and it is happening. The combination that is producing thos
EDIT: I got the date wrong! It's not Tuesday anymore. I released it MONDAY. This is how you know I've been busy! It was hard. It took a year. And I got two sales (from preorders) right off the bat. I am a standalone sci-fi thriller author. I will only write standalones. Each book (I say this with two out but around ten more in my head) is written to deliver a full, satisfying arc without requiring anything before or after it. Am I shooting myself in the foot because of lower read-through? Possib
Hi everyone! I recently collaborated with a writer and we've completed the first chapter of our manga. The story is genuinely strong and the visuals have come together really well we're both really proud of what we've created Now we're stuck on the next steps and could really use some guidance from people who've been through this before Here's what we're trying to figure out: Where should we publish? We want to find platforms where we can self-publish our manga and actually reach readers. We've
A few years ago, I started writing stories just for fun. I know I'm not a great writer, so it's always been more of a hobby than anything else. But with AI, some of those stories have actually started to take shape. I began imagining the characters more clearly, outlining plotlines, and putting together something that feels a bit more meaningful. I'm not saying any of it is particularly good, just that I'm genuinely enjoying the process and have invested quite a bit of time into it. Earlier this
I rebuilt our AP using AI last year and I am still figuring out parts of it as some weeks it runs without me touching it which is a weird feeling after years of being in every approval and the close on the AP side used to take 5 days but the last one was under a day although I am not sure how much of that is the new setup or if we're just having a calmer month. Even though it feels good to finally be able to offload this work I still dont think I can trust it yet when I spent so long being the b
What actually breaks after you deploy client automations? I’m curious about something that doesn’t get talked about much. Everyone shares clean automation diagrams, but I rarely see people talk about what happens 2–4 weeks after deployment. From what I’ve seen, automations don’t always “fail” loudly. Sometimes they keep running, but the business process is already broken. Examples: A webhook still fires, but the payload is missing an important field A Zap/Make/n8n workflow runs, but creates dupl
Everyone talks about AI and KDP publishing like it's either a goldmine or complete garbage. The reality I've found is more nuanced than either side admits. The writing barrier is genuinely gone. Tools exist now that handle full manuscript generation with consistent context across chapters, cover art, and KDP ready export with metadata included. That part actually works. What nobody mentions is that niche research still determines everything. The AI doesn't solve discoverability, it just removes
Hello everyone, I work with process automations and I currently encounter a challenge in attracting customers, specifically in the Spanish market. I have tried cold prospecting through Instagram and WhatsApp, but the receptivity is low. The vast majority of local businesses here (such as clinics or service businesses) already use closed third-party reservation systems for their web pages, which reduces immediate interest in personalised solutions. Currently, my main source of attraction is Upwor
Play it here (free, no download, headphones recommended): https://backroom-escape.vercel.app/ Find 8 pages scattered inside the maze, reach the exit, don't get caught. Works on desktop and mobile. The maze is built fresh each run using a recursive backtracker with loops and open rooms punched into it. The textures, the mono-yellow wallpaper, carpet stains, ceiling tiles, even the normal maps, are all drawn onto canvases in code. The audio is 100% WebAudio synthesis. Fluorescent hum, footsteps, t
(if you're looking for accurate information to publish a book then this not for you.) Hi everyone, As an indie authors, for some professional purposes, I did researches about the editorial rules and thought it might be useful for those who are about to publish their first book. Hope it can help. Here are the ten details that create trust for your readers. Most self-published books get at least three of them wrong: 1. Typographic quotes, not straight ones The difference between "like this" and "l
Over the time ive been using ChatGPT ive encountered an issue where it refuses to give me answers to test questions, whether it is a file a photo or a website. How do i make it to comply every time? Any help would be appreciated.   submitted by   /u/Difficult_Ladder_983 [link]   [comments]
Hi r/selfhosted , Disclosure: I built TypeType. Small disclaimer: no AI feed, no recommendation magic, no engagement tricks, no ads, no sponsors, no bullshit. The point is just to watch your videos peacefully and keep your own watch data on your own instance, links at the bottom u can launch everyhting in a single command ;p TypeType is a self-hostable video app for YouTube, NicoNico and BiliBili. The idea is to keep the personal part of video watching on your own instance: history, playlists, f
Lately, I’ve been manually converting all my research PDFs and DOCX files into clean Markdown before pasting them into ChatGPT or Claude. If you just copy-paste a raw PDF, you’re paying a massive "hidden layout tax." The model wastes thousands of tokens trying to parse layout trivia; broken hyphens, weird line breaks, headers, and footers. Not only does it bloat your dev budget and eat up your context window, but it actually degrades the output quality because the AI gets distracted by the junk
so some of my pages in my kids book have full pages with bleed some images don't and I can't help but think that my canva for the KDP pages are the wrong size? I am doing 6x9 book. the bottom page of the bleed pages is just a teeny tiny space I'm not sure how the heck to fix this? how do I find the exact inside size the 6x9 should be if it isn't?   submitted by   /u/strawbber81 [link]   [comments]
Hi, I'm dyslexic and have adhd but have been trying to write a book for years (not telling how many lol) only to have it a disorganised pile of notes spread over various bits of paper and electronic devices. Since I tried ai, I find it can do the bits I royally suck at, I want to stay firmly in the ai assisted category, not the ai generated category. Does anyone mind having a look through my rules to see if I need to add to make it as air-tight as possible. I spent quite a while sorting all this
I had this whole breakdown of a pricing strategy for my company . Spent maybe 40 minutes on it, really good stuff. Needed to reference it today and I genuinely cannot find it. Checked my personal ChatGPT, work ChatGPT, Claude personal, Claude work, gemini . Scrolled through probably 20 conversation titles. Nothing. Ended up just re-prompting from scratch which took another 30 mins. At this point I feel like I'm doing real thinking inside these tools but it just... evaporates. There's no way to s
I started my first job in the industry at the beginning of May and since then I’ve been given their log in to chatgpt. The chat history and ongoing chats are extremely demotivating. “Make me a marketing video”, “Make me a marketing campaign for this month long campaign idea”, “Make a description for this style product”, “Make me a shotlist for this photoshoot of a product”, “Make me a graphic to put on this style product”, and its ongoing. The projects I’ve been able to pump out for them blow th