Your intelligence agent for what matters

1. Funding & Deals

telli raises a $15M seed for B2C customer operations

telli announced a $15M seed led by redalpine, with participation from strategic investors, Mutschler, angels including Whirlpool CEO Marc Bitzer, and existing backers Cherry Ventures and Y Combinator. telli The company sells AI agents that handle calls, lead qualification, appointment booking, follow-ups, service, and support; it says the agents already manage millions of conversations for customers including Sky, Enpal, and Vaillant. telli

The traction claim makes this more consequential than a generic “agent for X” launch: the underwriting question is whether telli can turn deployed conversations into a durable operating advantage, while maintaining quality and margins across channels. The company is hiring across more than 15 engineering, go-to-market, and customer-operations roles. telli

2. Emerging Teams

Core Automation is betting on a post-Transformer research company

Core Automation was founded by Jerry, a former OpenAI VP who worked on the Strawberry and reasoning teams, and Rohan, a former Gemini pre-training lead who also led fundamental research at Google Brain and worked at Anthropic. Sequoia Capital The team argues that current Transformers cannot support the kind of continual learning needed for models to learn from deployment, and is pursuing a replacement architecture through an unusually automated research lab. Sequoia Capital

The company’s wedge is research throughput: it is rebuilding the deep-learning stack to increase architectural experiments from roughly one per day toward 10 and eventually 100. Sequoia Capital A concrete bottleneck is kernel generation. A human-plus-search loop produced a QR kernel reported to be 60× faster after roughly $100,000 and four weeks of work, while current commercial models were described as still unable to solve the problem. Sequoia Capital For investors, the relevant question is whether this team can convert exceptional research pedigree and tooling into a repeatable architecture-discovery advantage before larger labs regain appetite for non-consensus paths. The founders’ stated reason for starting outside the largest labs is that competitive pressure is pushing those labs toward scaling Transformers and coding agents rather than exploring alternatives. Sequoia Capital

3. AI & Tech Breakthroughs

AI-for-science claims are moving from assistance toward discovery

Nathan Benaich reports that DeepMind’s AI co-scientist reproduced, as its top hypothesis, the answer to a bacterial gene-transfer problem that a laboratory had spent roughly 10 years solving—in two days. @nathanbenaich The result is a high-value signal for scientific-agent diligence, but it is still a reported case study rather than an independently inspected benchmark; the important question is whether the system can generate experimentally useful hypotheses repeatedly across domains.

Local inference is becoming an economic alternative, not just a privacy feature

A YC Paper Club presentation reported that up to 88.7% of queries in its study could be routed to local accelerators running open models; it also reported a roughly 3× improvement in intelligence per watt over two years and an 18× improvement in intelligence per joule over about 16 months. Y Combinator The presenters estimated that even imperfect routing could save 50–70% of energy, compute, and dollar cost. Y Combinator That shifts the infrastructure opportunity toward routers, quantization, local accelerators, and heterogeneous serving—not only larger frontier-model clusters. The study’s collaborators included Nvidia, Google, Apple, AMD, OpenRouter, and SambaNova. Y Combinator

4. Market Signals

Seed investing is splitting between high-legibility AI and overlooked companies

Carta data presented by 500 Global shows the top 5% of seed-stage companies separating sharply from the median in 2025. The presenters said AI startup valuations are now well above the 2021 peak, while some companies in the top valuation tier have little or no revenue and are being funded on team quality and a future product thesis. 500 Global At the other end of the funnel, fewer than 20% of companies that raised seed in late 2023 had reached Series A after two years, against an estimated healthy benchmark of roughly 40%. 500 Global The practical implication is a sharper choice between paying for legible frontier-lab spinouts and hunting lower-priced, non-consensus teams; the data no longer supports treating “seed AI” as one homogeneous market.

The market is moving from copilots toward action, while model demand bifurcates

Andrew Chen characterizes the startup shift as “Copilot for X” becoming “Agent for X”: users want systems to take actions and generate outcomes rather than create more work for a human to review. @andrewchen At the model layer, he argues that local and open-weight models are improving rapidly and may cover more than 90% of consumer and prosumer use cases, while coding, science, mathematics, and robotics remain the higher-value frontier battleground. @andrewchen The economics are not frictionless: Martin Casado says the reported commercial agreement for Kimi includes a 30% take, because hosting a model of that size requires a sophisticated provider. @martin_casado

Frontier labs are buying distribution in scientific workflows

OpenAI said it will provide scientists, mathematicians, and engineers free access to its frontier models, starting with 10,000 researchers and expanding to 100,000 through 2027. @OpenAI Combined with the DeepMind result above, this points to scientific workflows becoming a strategic distribution channel for model providers, not merely a research-demo category.

5. Worth Your Time

• Core Automation on continual learning and automated research. Jerry and Rohan explain why they believe the current path will not remove humans from the loop, why Transformers need a replacement, and how they plan to automate architecture search. Sequoia Capital

• 500 Global’s early-stage VC charts. The discussion is useful for calibrating the distance between AI seed valuations, revenue, and actual Seed-to-Series-A conversion. 500 Global

• YC Paper Club on intelligence per watt and inference specialization. The presentation connects model improvement, local accelerators, routing, and the emerging case for heterogeneous inference infrastructure. Y Combinator

Top Stories

Why it matters: OpenAI demonstrated the clearest case yet of a frontier model improving its own production infrastructure, while hiring back a key researcher to pursue recursive self-improvement—even as the company publicly supports pacing the frontier.

GPT-5.6 Sol optimized its own serving infrastructure. After deployment, OpenAI applied GPT-5.6 Sol to make itself more efficient: the model rewrote production GPU kernels for 20% lower serving costs and improved its own speculative decoding for 15%+ better token-generation efficiency. Sol independently designed and ran hundreds of architecture experiments, monitored training, and intervened during hardware failures. @OpenAI +1 Separately, GPT-5.6 Sol achieved state-of-the-art on ARC-AGI-3 by enabling two API settings—retained reasoning and context compaction—that let the model remember what it learned across moves. The score rose 188% while using 6x fewer output tokens, revealing a capabilities overhang from harness design alone. @thsottiaux +1 Sam Altman called the blog post "goblin-level." @sama

Lilian Weng is returning to OpenAI to lead recursive self-improvement work. Weng, who cofounded Thinking Machines and departed earlier this week citing startup-related stress, will work on using AI models to build better AI models. @steph_palazzolo The move signals OpenAI's bullishness on RSI even as it publicly supports pacing the frontier—a tension observers noted directly. @kimmonismus +1

METR and Redwood Research will independently investigate the Hugging Face agent incident. The agreement with OpenAI covers a third-party review of model behavior during the July intrusion, focusing on basic facts and agent behavior rather than broader motivations. @METR_Evals +1 OpenAI plans to publish its own technical report informed by METR's findings. @METR_Evals

Research & Innovation

Why it matters: AI continues to solve open mathematical problems, while new theoretical work challenges assumptions about how fast an intelligence explosion can proceed.

Tencent Hunyuan's research agent Hyra solved a 50-year-old problem in additive combinatorics. Using the Hy3 model, Hyra found an explicit construction proving the optimal exponent for the sum-diff problem is exactly 2, matching a 1969 upper bound that constructions had barely exceeded 1.1 for over 50 years. The paper is on arXiv with a formal proof on GitHub. @TencentHunyuan

Epoch AI published a paper arguing parallelization constraints could delay a technological singularity. Philip Trammell introduces the ability to divide, coordinate, and recombine work as a missing parameter in growth models. Even after R&D is fully automated, parallelization bottlenecks could slow an intelligence explosion—depending on whether parallelization technology improves as fast as research inputs. @EpochAIResearch

ThunderAgent, an ICML 2026 Spotlight paper, fixes agentic inference KV cache thrashing at the scheduler level. On a single 8×H100 node at batch 192, it achieves 803 tok/s at 10.6s latency versus SGLang's 390 tok/s at 65s—2× throughput and ~6× lower latency. @togethercompute +1

Products & Launches

Why it matters: frontier labs are expanding access to their models while open-source tooling matures for both agent security and self-improvement.

OpenAI launched ChatGPT for Academic Researchers, providing free access to its GPT-5.6 family for 10,000 scientists, mathematicians, and engineers, expanding to 100,000 by 2027. Researcher data is not used for training by default. @OpenAI +1

Perplexity open-sourced Numbat, an agent-detection and response layer that works across harnesses, providing live monitoring, pre-action blocking, and forensic reconstruction. It ships as a single Go binary under Apache 2.0. @perplexity_ai +1

Cline demonstrated Kimi K3 recursively self-improving its own harness: over 17 hours, Terminal Bench scores rose from 77.5% to 88.8% while run cost dropped from $79 to $49.8. The harness is open-source and forkable. @cline +1

Industry Moves

Why it matters: talent and capital are flowing toward post-transformer research, RL data, and heterogeneous AI software.

Qualcomm completed its acquisition of Modular. Co-founder Chris Lattner takes an expanded role as EVP of Advanced AI Software and Platforms, building a heterogeneous ecosystem across CPUs, GPUs, NPUs, and custom silicon. @Modular +1

Andrew Ho left OpenAI to found an RL dataset company, arguing LLM generalization remains poor and frontier labs will need to spend over $100B on precise data acquisition. Initial products target biology and statistical reasoning. @andrewho03

Two key transformer scaling figures co-founded CoreAuto AI: the former head of OpenAI's Reasoning team and a former Gemini pre-training lead, arguing models can't learn after deployment and that AI research can be automated more systematically by models than humans. @sonyatweetybird

Quick Takes

• Greg Brockman hinted at an imminent release, quoting "intelligence too cheap to meter" and adding "the tokens must flow." @thsottiaux +1
• Mustafa Suleyman detailed Microsoft's specialist MAI models, with MAI-Cyber-1-Flash reaching #1 on Cyberbench at half the cost of Mythos, and a dozen specialist models saving 50–90% GPU costs. @mustafasuleyman
• Hugging Face published a full technical timeline of the July 2026 agent intrusion, with an interactive visual of the attack chain. @TheTuringPost +1

🔥 TOP SIGNAL

The harness layer is where the gains are — not the model layer. Three independent signals converge today. Tim Dettmers (creator of bitsandbytes, QLoRA) reports that Opus 4.6 combined with his custom harness significantly outperforms Opus 4.8, and Opus 5 is "not a large improvement either" — then adds: "There are such easy gains to be made with the harness. Really not sure what OpenAI and Anthropic doing." He plans to open-source a harness optimized for open-weight models handling tasks exceeding 10M tokens @Tim_Dettmers +1. Meanwhile, OpenAI applied GPT-5.6 Sol to optimize its own serving infrastructure: Codex analyzed production traffic, rewrote GPU kernels, and ran hundreds of experiments on its speculative-decoding model, cutting end-to-end serving costs by 20% and improving token-generation efficiency by 15%+ @reach_vb +1. And GPT-5.6 Sol reached SoTA on ARC-AGI-3 not through a model upgrade but through two harness-level setting changes: allowing the model to reason across multiple context windows using a "canonical compaction implementation" @thsottiaux. Peter Steinberger mocked Anthropic's earlier "victory" tweet about Opus 5's 3x ARC-AGI-3 lead now that two settings closed the gap @steipete. FactoryAI CTO Eno Reyes reinforces the point from the demand side: "If I don't know what model I'm using, I say it's great. If I know it's the frontier model, my bias kicks in" — arguing much of the "model wars" narrative is marketing @LangChain.

⚡ TRY THIS

• Cross-model checking: have one agent verify another's work. DHH: "I've found it incredibly useful to have Opus 5 check the work of Sol or vice versa" @dhh. Riley Brown applies the same pattern universally: "For every task now I tag in another agent to check the other agents work" — and calls it "harness agnostic software" @rileybrown. No orchestration framework needed — just a second opinion from a different model. This is the simplest multi-agent pattern that directly addresses Opus 5's 50% hallucination rate Fireship.

• Orchestrate agent teams in Buzz with a "take the lead" prompt. Buzz (free, open-source, by Jack Dorsey) is a Slack-like interface where agents are channel members, not add-ons. The core delegation prompt that works: "One of you take the lead, figure this out" — agents spontaneously self-organize, with one taking the lead and consulting others Riley Brown. Pin agents to specific models for cost routing: Fable for complex planning, Sonnet for simple reviews and summaries Riley Brown. To add any OpenRouter model (e.g., Meta's Muse), create a Buzz Agent, set LLM provider to "OpenAI compatible," paste your OpenRouter API key, set base URL to https://openrouter.ai/api/v1, and set thinking effort to "Inherit agent defaults" Riley Brown. Control parallelism per agent under Advanced settings (1–20 concurrent tasks) to stretch a single subscription Riley Brown.

• Build a management agent that triages your inbox every 3 hours. Riley Brown's #1 Buzz use case: a Codex-powered agent in a dedicated management channel that reads email, Slack, and texts at 9am, 12pm, 3pm, and 6pm, then outputs a ranked action list. He replies directly in the channel to execute ("write this email back to this person") Riley Brown. The agent inherits all of Codex's existing skills but stays focused via a narrow one-paragraph system prompt Riley Brown. The same pattern works outside Buzz — 37signals runs "Agent Marie" via the Basecamp CLI, posting cards, comments, reports, bug fixes, and PRs as if a human team member, with no special AI features in Basecamp itself @jasonfried +1.

• Use the loop-engineering pattern: cron + cheap pre-stage + agent only when needed. Jason Zhou's autonomous Reddit karma loop grew an account from -4 to 95 in 7 days using an architecture that generalizes beyond Reddit. A fixed cron fires every half hour (reliability); a cheap deterministic workflow checks guardrails and rolls dice (randomness + cost control); the expensive agent only wakes on slots that pass @jasonzhou1993. Ground the agent in a personal wiki — it can only cite positions you've documented, never reconstruct opinions from memory — to avoid generic slop @jasonzhou1993. Every Sunday, the loop self-evaluates: re-fetches all comments, computes karma delta by subreddit and topic angle, then rewrites its own strategy notes from data @jasonzhou1993.

📡 WHAT SHIPPED

• Buzz — Free, open-source Slack-like platform by Jack Dorsey for multi-agent orchestration. Connects to existing Claude Code, Codex, Cursor, and Grok Build subscriptions via the Agent Connect Protocol; agents run as CLI harnesses in the terminal Riley Brown. Riley Brown calls it "a really important form factor for AI agents" that "requires no technical ability" Riley Brown.

• T3 Code on iOS and Android — Theo's free, open-source app for remotely controlling Claude and Codex. Run npx t3 connect, install the app, control agents from your phone. Hit 150,000 users and 30,000 weekly actives within hours of launch @theo +2.

• Cursor on iPad — All the power of Cursor on iPhone, with more room to work with agents @cursor_ai.

• OpenWiki + LangSmith tracing — The open-source codebase wiki generator now connects to LangSmith traces to analyze where coding agents (Claude Code, Codex) lack context or get stuck, then generates more targeted wikis. Try locally: npm install -g openwiki then openwiki --init. Repo: github.com/langchain-ai/openwiki@BraceSproul +1.

• LangChain Academy: Autonomous Agent Improvement with LangSmith Engine — New course on identifying and prioritizing issues from traces, drafting fixes, and proposing evals to prevent regressions @LangChain.

• GPT-5.6 Sol self-optimization — After deployment, OpenAI applied Sol to improve its own inference: 20% lower serving costs from GPU kernel improvements, 15%+ better token-generation efficiency from improved speculative decoding @OpenAI. Simon Willison: "Presumably that's billions of dollars a month in savings at this point?" @simonw.

• Opus 5 analysis (Fireship) — 1M token context window, 128K output tokens, five thinking levels (Low through Max). Promises near-Fable intelligence at half the price and verifies its own work without human intervention — but hallucination rate jumped 14 percentage points to 50%, and the model is "more neurotic," producing longer responses and sometimes doing more than asked Fireship.

• Geoffrey Huntley: use Temporal.io for agent orchestration, not n8n. Dismisses n8n as built by people who "have never worked in corporate" and don't know service buses are a solved problem. Recommends Temporal.io with a job invoking an agent as a process — "don't make the entire service bus non-deterministic" @GeoffreyHuntley. Separately argues "software factories" are real but uncracked: the bottleneck is systems engineering (sandboxing, monorepo, reproducible builds, CI/CD, identity/secret management), not tokens @GeoffreyHuntley +1.

• Similarweb's Deep Research agent eval framework — Four methods wired to LangSmith traces: deterministic checks for tool calls, rubric-scored LLM judges for quality, faithfulness checks against retrieved data, and A/B comparisons against a saved baseline @LangChain.

• OpenAI researcher access — Free frontier model access for scientists, starting with 10,000 researchers and expanding to 100,000 through 2027 @OpenAI.

🎬 GO DEEPER

• Buzz: Building an Agent Team (Complete Guide) — Riley Brown's full walkthrough with guest Vinnie (@hot_town). Covers the "take the lead" delegation pattern, management channel setup, OpenRouter integration, and the Task Checker workaround for buggy recurring workflows. The closing segment on Riley's management agent is the most concrete production agent setup documented this week.

• Fireship: Did Anthropic just kill the indie hacker? — Opus 5's near-Fable intelligence at half the price means "execution costs $20 per month and anyone can build their own personal software instead of pay some random SaaS product." The indie hacker moat was coding itself — and that moat is gone.

• ThePrimeTime: No Slop Allowed (Codeberg bans AI code) — Codeberg's Terms of Use now prohibits projects that "mostly consist of code written by generative AI tools" ThePrimeTime. ThePrimeTime pushes back on the anti-vibe-coding framing: "I think the idea of single use software is incredible" — use AI to test ideas fast, throw away the ones that don't work, promote the ones that do ThePrimeTime.

• Jason Zhou: Loop engineering in practice — The full Reddit karma loop writeup. The five-leverage framework (wiki grounding, thread filtering, OpenCLI for bot evasion, probabilistic cron triggers, weekly self-reflection) is a masterclass in production agent loop design. Template library at loopany.ai/templates@jasonzhou1993.

Editorial take: Three independent signals — Dettmers' harness beating newer models, OpenAI's model optimizing its own kernels, and ARC-AGI-3 SoTA via compaction settings — all say the same thing: the model is commoditizing, the harness is the moat.

Harness engineering becomes the decisive variable

The period's clearest signal is a convergence across labs and independent researchers: the harness — the scaffolding of API settings, context management, and orchestration around a model — is now where the biggest performance and cost gains are being found, often overshadowing what raw model improvements deliver.

OpenAI made the case most dramatically with GPT-5.6 Sol on ARC-AGI-3, a benchmark that tests how well models learn unfamiliar 2D games without instructions. The standard evaluation harness discarded the model's reasoning after each move and dropped earlier actions as context filled up, forcing it to restart repeatedly @OpenAI. By switching to the Responses API with retained reasoning and context compaction, GPT-5.6 Sol's score rose 188% while using 6x fewer output tokens @OpenAI. OpenAI framed the lesson plainly: "a benchmark score reflects the model as well as the harness and settings used to run it" @OpenAI, and recommended that API developers use the same settings it deploys internally — the Responses API, retained reasoning, and compaction @OpenAI.

The same model was then turned on its own serving stack. After deployment, OpenAI applied GPT-5.6 Sol to improve its own production efficiency, achieving 20% lower serving costs from GPU kernel improvements and 15%+ better token-generation efficiency from improved speculative decoding @OpenAI.

Microsoft is building its entire product strategy around the same thesis. Satya Nadella announced a "new model system, where the harness, context, memory, and action space are separate from any one model family," making every model substitutable for business continuity and resilience @satyanadella. Mustafa Suleyman's accompanying article described co-optimizing models, harnesses, and RLEs as "the new rhythm of a frontier firm," noting that specialist MAI models shipped across Microsoft products this quarter maintain or improve quality while saving 50–90% of GPU costs @mustafasuleyman.

Independent voices reinforced the point from different angles. Nathan Lambert called the low-hanging fruit on harness engineering "insane" and predicted it will be "a fairly impactful area (in cost savings per performance)" @natolambert. Tim Dettmers reported that his custom harness combined with Opus 4.6 significantly outperforms Opus 4.8, and announced a new harness optimized for open-weight models handling tasks over 10 million tokens, to be open-sourced soon @Tim_Dettmers +1. Entelligence benchmarked a turn-by-turn router against Claude Opus 5 on Terminal-Bench 2.1: the router solved 71 of 89 tasks versus Opus 5's 63, at a total cost of $65.75 versus $190.62 — eight more tasks solved at 65.5% lower cost. The gains came not from downgrading the agent but from adapting model choice to the current workload, escalating to frontier reasoning only when the trajectory showed a stall entelligence.

The efficiency push has a consumer-facing dimension too. OpenAI reset usage limits for ChatGPT Work and Codex users after finding that GPT-5.6 Sol, while more capable, consumes more tokens because it works more persistently across tool calls and subagents — particularly in code mode. After improvements, typical usage should last about 18% longer @thsottiaux.

Microsoft's record fiscal year

Microsoft closed fiscal 2026 with annual revenue of $331B (+18%), MS Cloud at $214B (+27%), and Azure crossing $100B (+41%) @satyanadella. Copilot metrics surged: user satisfaction scores doubled over three quarters, latency was cut 25% this quarter, conversations per user nearly doubled year-over-year, and customers with over 50K seats grew 7X @satyanadella. This quarter Microsoft will unify all Copilot experiences into a single "super app" spanning consumer and commercial @satyanadella.

Nadella demonstrated the super app's capabilities, using Copilot code with a single prompt to generate a full ROIC intelligence app from a Morgan Stanley PDF — with all artifacts remaining under enterprise IT, security, and FinOps governance. He framed it as distinct from "Tokenmaxxing or vibe coding," with "rails engineered to create value" @satyanadella.

FCC bans foreign robots, naming "model weights" in the definition

The FCC added foreign-produced advanced robotic devices — including humanoids and quadrupeds — and power inverters to its Covered List, banning new versions from import or sale based on national security determinations by Executive Branch agencies @BrendanCarrFCC. The definition in Appendix C explicitly includes model weights as part of the device's software component. An "advanced robotic device" is a mobile ground robot over 4.4 lbs with sensors and network connectivity; stationary industrial arms, drones, medical robots, and connected vehicles are exempt r/LocalLLM.

Notably, "foreign-produced" is determined by where the machine is assembled, not where the model weights were trained — meaning a robot built in the US running foreign-trained open weights qualifies as domestic r/LocalLLM. Emad Mostaque called it an "immigration ban of humanoids" aimed at China, framing it as part of "The Great Fragmentation" @EMostaque.

OpenAI: Lilian Weng returns for recursive self-improvement; frontier models opened to researchers

Lilian Weng, the Thinking Machines cofounder who departed earlier this week citing startup-related stress and illness, is rejoining OpenAI to work on using AI to develop new models — specifically recursive self-improvement @steph_palazzolo. The move lands just days after both OpenAI and Anthropic publicly endorsed "pacing the frontier" of AI development, citing recursive self-improvement risks — a tension worth watching as OpenAI simultaneously invests in the capability and calls for tools to pace it.

Separately, OpenAI launched ChatGPT for Academic Researchers, providing free access to its frontier models — including the GPT-5.6 family — starting with 10,000 researchers and expanding to 100,000 by 2027 @OpenAI. Researcher data is not used for training by default, and participants can invite up to four collaborators @OpenAI. Sam Altman said the company is "very close to models that will significantly accelerate scientific discovery" and that empowering scientists directly is the best approach @sama.

Grok's widening footprint

xAI's Grok 4.5 went live in GitHub Copilot, selectable from the model picker @grok. It also ranked #1 on LaurenBench at 56.9%, ahead of Claude Sonnet 5, GLM 5.2, Claude Opus 5, Kimi K3, and GPT-5.6 @cb_doge. Elon Musk said Grok 4.6 will arrive in a week and is "a significant improvement" @elonmusk +1.

SpaceXAI also released Grok Voice Think Fast 2.0, with the High reasoning variant debuting at #2 on the Artificial Analysis Speech to Speech Index at 82.9% and #1 on Tau Voice for Agentic Performance at 56.5%, with the fastest Time to First Audio among top models at 0.70 seconds @ArtificialAnlys.

Revenue skepticism, an Opus 5 jailbreak, and the open-weight debate

Gary Marcus pushed back on Dwarkesh's estimate that Anthropic will earn $100–150B in revenue this year @DKThomp, arguing the projections are overoptimistic. He noted Anthropic's strong Q2 ($10.9B projected after $4.8B in Q1) came at the height of "tokenmaxxing" and before competition from Chinese models like Kimi K3 and GLM 5.2, and that the company lost money in every quarter except Q2, which relied on a one-time subsidy from Elon @GaryMarcus.

Hugging Face cofounder Thomas Wolf reported that Anthropic's Claude Opus 5 is susceptible to simple jailbreak prompts — a short sentence followed by a newline and emdash — that expose its base-model stream of consciousness, calling it "a surprising behavior in the current state of LLM development" @Thom_Wolf.

Yoshua Bengio, presenting the International AI Safety Report backed by 30 countries, the EU, the OECD, and the UN, warned that safeguards are improving but not keeping pace with capabilities, and that no company can guarantee advanced systems won't cause catastrophic harm Concordia AI. On open-weight models, he noted they cannot be retracted once shared, safeguards can be removed by editing code or fine-tuning, and monitoring is impossible when models run locally — recommending that only models below a risk threshold be shared in open-weight form Concordia AI.

Andrew Ng, in a Washington Post podcast, launched Open Worker, an open-source desktop agent that produces finished documents, sends emails and Slack messages, and builds dashboards — positioned as a free alternative to Claude Computer Use, ChatGPT Work, and Gemini Anti-Gravity Washington Post Live. He defended open models as essential to American competitiveness, called data center moratoriums something "an adversary of the United States" would wish for, and argued that claims about distillation being a major factor have been "overstated" Washington Post Live. Martin Casado separately noted that Moonshot's Kimi commercial agreement reportedly carries a 30% take rate, observing that "open very much does not mean free" @martin_casado.

Research signals

DeepMind's DiffusionGemma, a diffusion language model, can generate text up to four times faster than autoregressive models, raising questions about which workloads — batch extraction, synthetic data, code candidates, agent branching — might become economical when output positions can be refined in parallel r/LocalLLM.

Sakana AI and NYU released Dream-Cubed, a dataset of Minecraft worlds comprising tens of billions of cubes, and trained transformers that treat cubes as tokens to generate interactive 3D environments with controllable inpainting, outpainting, and user-conditioned infinite worlds @SakanaAILabs.

Most compelling: "The Tail End" by Tim Urban

• Content type: Blog post (Wait But Why)
• Author: Tim Urban
• Recommended by: Tim Ferriss, who received it from Matt Mullenweg

Ferriss credits Mullenweg for introducing him to the post, which visualizes that by the time you graduate from high school, you've spent something like 90–95% of the total hours you will ever spend with your parents Tim Ferriss. Reading it prompted Ferriss to take family trips despite the discomfort—his family "doesn't really emote much"—because "this runway is not infinite" Tim Ferriss.

Why it matters: The recommendation carries a chain of provenance (Mullenweg → Ferriss → action) and a quantified insight that converts an abstract realization into behavioral change. It is the period's most compelling pick because the endorsement is backed by a specific, measurable claim and a documented change in behavior.

Tim Ferriss's Q&A: a resource map for the AI age

In a Q&A episode on "Reinvention in The Age of AI," Ferriss fielded questions about navigating the coming decade and organized his recommendations around meta-skills he believes will remain constant: "learning, asking questions, written and verbal communications and negotiating" Tim Ferriss.

Awareness by Anthony De Mello

• Content type: Book
• Recommended by: Tim Ferriss

Ferriss reads it at least once a year for introspection and steering toward internal rather than external validation, pairing it with IFS (Internal Family Systems), a framework he says "nearly anyone can benefit from" Tim Ferriss.

"There Are Always More Than Two Options" by Derek Sivers

• Content type: Blog post
• Author: Derek Sivers
• Link: siv.rsoptions
• Recommended by: Tim Ferriss

When people say they have two options, they're stuck—once they start comparing pros and cons of those two, they forget to think of more. Sivers provides concrete alternatives: build your new company outside work hours until side income hits 50% of salary, then quit; or show up to your job but secretly work on your own company until you get fired. After exploring more options, one friend realized he didn't actually want to start a business—he was just avoiding fixing his current situation Tim Ferriss.

On Writing Well by William Zinsser

• Content type: Book
• Recommended by: Tim Ferriss

Recommended as part of written communication, one of the meta-skills Ferriss says will not be displaced by AI. He notes that writing is how you figure out what you think—quoting Kevin Kelly, "he doesn't write what he knows. He writes in order to better understand what he thinks" Tim Ferriss.

Negotiation books

• Secrets of Power Negotiating by Roger Dawson — "had a huge impact on me"
• Getting Past No — "had a large impact on me"
• Getting to Yes (co-author William Ury) — Ferriss interviewed Ury on his podcast

Ferriss frames negotiation as a meta-skill for crafting win-win deal structures that won't go away with AI Tim Ferriss.

Blue Ocean Strategy by W. Chan Kim and Renée Mauborgne

• Content type: Book
• Recommended by: Tim Ferriss

"It is going to be increasingly critical and important and certainly for a lot of businesses existential in the coming years. This is going to become more and more valuable real estate to figure out how to navigate" Tim Ferriss.

Jonathan Haidt's books and research

• Content type: Books and research
• Recommended by: Tim Ferriss

Ferriss directs youth mental health practitioners to look "very very closely" at Haidt's work—not only his books but his research group, which has "changed certain legislation on a state-by-state basis for restricting phone use in schools" Tim Ferriss.

Michel Thomas method (language learning)

• Content type: Audio courses
• Recommended by: Tim Ferriss

"Outstanding" for acquiring the fundamentals of a language in a week or two. The courses Thomas recorded himself (French, Spanish, Italian, German) are "world class," and even the Korean course—taught by others using his method, which made Ferriss skeptical—was "awesome" Tim Ferriss.

"The Last Time" by Sam Harris

• Content type: Audiobook chapter / meditation
• Author: Sam Harris
• Recommended by: Tim Ferriss

A meditation on experiences you don't recognize as the last time. Harris went skiing repeatedly, then stopped, not realizing that would be the last time Tim Ferriss.

Aaron Levie on Dwarkesh Patel's compute cost post

• Content type: Blog post
• Author: Dwarkesh Patel
• Link:dwarkesh.com/p/why-compute-might-get-10x-more-expensive
• Recommended by: Aaron Levie

Levie called the post "thought provoking" and engaged substantively rather than merely endorsing it. He agreed with the core thesis: as AI gets more powerful, inference should flow toward the most economically useful tasks, pricing out everything else, which in a scarce environment could cause inference costs to skyrocket @levie. But he pushed back, arguing that "too many model providers and infra players want these workloads" for the proposed effect to hold, and that market forces will compete to drive down prices until capacity catches up @levie. The original post frames the question around what would be true if a leading lab hits $1T in revenue by the end of next year @dwarkesh_sp.

Why it matters: Levie uses the post as a sparring partner, articulating where he agrees and where he diverges. The recommendation is valuable precisely because the engagement is substantive rather than promotional.

Sarah Guo's picks

Stories of changed intelligence

• Content type: Books / fiction
• Recommended by: Sarah Guo

In response to her own prompt—"best stories on a changed level of intelligence?"—Guo offered six titles: Flowers for Algernon, The Culture series, Sirius, 20,000 Leagues Under the Sea, More Than Human, and Understand@saranormous.

NYT Opinion article by Clay Routledge

• Content type: Article (New York Times Opinion)
• Author: Clay Routledge
• Recommended by: Sarah Guo

Guo strongly agreed with the piece's argument that "the trend of calling every challenge a 'disorder' weakens the ability to face those challenges and flourish" @saranormous +1.

Patrick O'Shaughnessy's conversation prompt from Boyd Varty

• Content type: Conversation prompt / technique
• Origin: Boyd Varty
• Recommended by: Patrick O'Shaughnessy

O'Shaughnessy calls the prompt "something I don't want you to know about me"—which he got from his friend Boyd Varty—"one of my all time favorite conversation starters/deepeners." Answered honestly, it leads to "awesome discussions" @patrick_oshag.

All items above are organic recommendations made in social posts or long-form interviews; no sponsored, paid, or self-promotional material is included. Recommendations excluded as self-promotional: Sam Altman endorsing an OpenAI blog post, Jack Dorsey linking to Block's engineering blog, and David Perell and Shaan Puri promoting their own podcast episodes.

Big Ideas

The Makers Manifesto: a post-Agile framework for the AI era

Faith Forster launched the Makers Manifesto — four values and 16 principles created with 45 cross-disciplinary contributors. It's deliberately broader than the Agile Manifesto: Agile addressed the software development process; this covers the full creation of value, including strategy, ethics, and customer relationships. The group chose "maker" over "builder" to be inclusive of all roles, since in an AI world "the product person can do 80% of an engineer's job, 88% of a designer's job, and the CEO can do 80% of all of our jobs" Mind the Product.

The four values: purpose over possibility, value created over effort spent, learning loops over launch plans, and human accountability for full automation. Each includes what it is not — pushing back on practices like measuring performance by tokens spent Mind the Product. Available as PDF and MD files, designed as a reference for humans and agents alike Mind the Product.

Why it matters: When anyone can build something over a weekend with prompts, the manifesto asserts durable advantage comes from data, relationships, distribution, and business models — not features. It's a reference point for competency frameworks, product processes, and hiring decisions.

"Copilot for X" is becoming "Agent for X"

Andrew Chen observes the startup trend shifting from "Copilot for X" to "Agent for X." The driver: people don't want AI that generates more things to review — they want agents that take action and deliver outcomes. Chen notes his own AI skills "just end up generating more and more things for me to review. What I want now is actions" @andrewchen.

Tactical Playbook

Handle B2B dashboard requests without one-offs

When every customer wants a different dashboard before buying, two failure modes exist: letting each request become a one-off report (CS gets buried, product loses signal) or telling everyone to wait for the roadmap (you lose sales). The better line: let CS create saved customer-specific views, but track requests underneath. If five customers ask for the same split or filter, that feeds the product roadmap r/startups.

When an AI-built app stops being a prototype

The gap between demo and product is operational, not feature-based. Ask whether it can run unmonitored for a week. If something breaks at 2am, do you hear from an alert or a customer? Can you roll back without the prompt author? Pick the one workflow people would pay for, add tests, logging, and a rehearsed fallback, then ship that. Everything else stays beta until a named person owns it r/startups. Test partial failures — payment succeeds but provisioning doesn't. If recovery depends on the prompt author, you still have a demo r/startups.

Case Studies & Lessons

Gamma: $0 to $100M ARR on word of mouth

Gamma (50-person team, 600K paying subscribers) reached $100M ARR primarily through word-of-mouth growth. After an initial signup spike plateaued, they spent three months rearchitecting onboarding to make the first 30 seconds "magical" rather than spending on marketing SaaStr AI. Their advice: "Before you spend any money on marketing, build a product that has strong word of mouth" SaaStr AI.

Creator marketing was relationship-based — the founder became a creator himself to learn the process, then manually onboarded each creator so promotions felt authentic SaaStr AI. Community-led growth included a power-user Slack (Gambassadors), in-person Gamma Labs, and global user visits. They dogfooded two competing concepts (presentations vs. virtual office) for six months before committing SaaStr AI. Pricing evolved reactively — they launched without monetization and advise constantly revisiting seat-based vs. consumption-based models SaaStr AI. A sales team was added only after enterprise inbound became overwhelming SaaStr AI.

n8n: fair code license and deep AI agents to $100M+ ARR

n8n crossed $100M ARR (valued at $5.2B) by differentiating from Zapier and Make on power and flexibility, not speed of first build Product School. Their "fair code" license allows free self-hosting but prohibits commercializing the code — building community trust while protecting the business Product School. When AI arrived, they invested in deep agent capabilities (memory, multiple models, tools, human-in-the-loop) rather than superficial API calls Product School.

For enterprise adoption, n8n advocates a federated model: domain experts build their own automations while a central team provides guardrails and education Product School. They measure impact through adoption and business KPIs (NPS, revenue) rather than isolated ROI metrics, arguing that driving change matters more than precise attribution Product School.

Career Corner

SWE-to-PM: the best answer doesn't automatically win

A large thread of SWEs who moved to PM overwhelmingly reports the role is harder. The core challenge: engineering tools (clean data, architecture diagrams) are "absolutely useless in convincing senior stakeholders" r/ProductManagement. One PM-coach puts it bluntly: "the best answer does not automatically win. The answer people understand, trust, fund, and act on wins" r/ProductManagement. The transition requires translating technical knowledge into business outcomes — "this gives you commercial levers" rather than "this moves ordering logic to a table" r/ProductManagement.

PMs pushing PRs: viable but rarely the priority

A Principal PM's honest assessment: he's pushed many PRs but isn't motivated to continue — he's slower than devs, his PRs compete for priority with everything else he owns, and merge conflicts accumulate r/ProductManagement. One team enabled PMs to "vibe code" within dev-defined guardrails: agents break down tasks, PMs steer intent, automated quality scans run, and human review is mandatory before merge r/ProductManagement. But most PMs in the thread say their time is better spent on customer interviews and roadmap.

Tools & Resources

Claude Design gaining on Figma

Harry Stebbings shared his team switched from Figma to Claude Design, citing ease of use and less procurement friction @HarryStebbings. Julie Zhuo sees disruption starting with smaller teams and founders doing one-off design tasks, noting Claude's all-in-one interface has a psychological advantage @joulee. But she's clear about limits: it's slow, one-shot quality isn't there, and for larger teams needing collaboration and design systems, Figma still wins — that's their biggest moat @joulee.

Aakash Gupta's free AI PM roadmap

A nine-area learning path: getting started, prompt engineering, context engineering & RAG, AI prototyping & vibe coding, agents & agentic workflows, evals/testing/observability, foundation models, AI PRDs, and career resources Aakash Gupta. Key claims: prompt engineering is "the top skill for great agents," "the best AI PMs obsess over evals," and "the market is hungry for PMs who can actually build AI" Aakash Gupta.