Your intelligence agent for what matters

Funding & Deals

• Walden Robotics raised a $300M seed round at a $1.1B valuation, co-led by Deviation Capital alongside Toyota Motor Corp., Toyota Invention Partners, and Toyota Ventures. The company spun out of Toyota Research Institute in January; CEO Russ Tedrake previously led TRI’s robotics and machine-learning team, and brought contributors to Diffusion Policy, Large Behavior Models, OpenVLA, and the Drake simulator. Its robots are already operating in a North American Toyota factory. The investment thesis is a full-stack robotics platform—hardware, software, models, and deployment applications—that learns manufacturing and logistics tasks from demonstrations and practice. Not Boring by Packy McCormick

Emerging Teams

• Imbue’s “Minds” is an early product signal for agent-native personal software. In an early-access event, Imbue described a tool for creating personal, malleable software that agents can use. Examples shared by CEO Kanjun include an email tool that reduced one inbox from 3,124 to 28 messages, a thought-capturing todo app, and a working Minecraft build made in two prompts. These are product examples shared by the company, not independently validated outcomes. @kanjun

• Agent API Gateway is testing a narrow infrastructure wedge for web-enabled agents. Its builder says the REST API converts public URLs into validated JSON for product, article, and company schemas, avoiding browser farms, proxy rotation, and CSS-selector maintenance. The project reported 16,800+ requests and 99.9% uptime in its first 24 hours, with a free tier and a $1 starter pack; these are founder-reported metrics. r/SideProject

• Prompt Compass targets lightweight, local guardrails and routing. The solo founder says its 0.57 MB model routes prompts to cheap or expensive models, detects PII, and catches jailbreaks; it reportedly runs in roughly 5 ms on CPU without a GPU, with an 82% accuracy trade-off. The claimed browser-, phone-, and edge-worker-sized deployment is notable for teams seeking a low-cost policy layer rather than another hosted model dependency. r/SideProject

AI & Tech Breakthroughs

• A Calico–Revel Pharma research effort points to AI-assisted enzyme design for extracellular-matrix targets. An All-In discussion reports that researchers used AlphaFold to identify a CML-binding protein, then iteratively altered DNA-programmed variants and screened hundreds to thousands of candidates over five cycles. The speakers report 52–97% CML clearance across tested proteins and 55% clearance in skin samples from donors over 70; these results warrant review in the underlying publication and are not clinical outcomes. All-In Podcast

• Postmint illustrates a pragmatic architecture for making generative marketing graphics usable. Its founder discarded an initial prompt-to-image prototype because of unreliable layouts and invented UI elements, then separated ideation from a deterministic rendering step, applied brand colors in code, and added automated checks that regenerate outputs with empty space, unreadable text, or fake UI. The product is live with a free tier; its broader relevance is the shift from prompt-only generation toward constrained production systems. r/SaaS

Market Signals

• Kimi K3 challenges the assumption that open weights automatically mean low-cost inference. Moonshot prices K3 at $3 per million input tokens and $15 per million output tokens, or roughly $5.40 blended under an 80/20 input/output mix—materially closer to frontier-model pricing than prior open-weight examples cited in the analysis. Clouded Judgement

  Self-hosting may not restore the historical discount: the model’s weights are estimated at roughly 1.4 TB, and Moonshot’s launch guidance recommends supernodes with 64 or more accelerators for effective serving. Clouded Judgement’s conclusion is appropriately conditional: third-party price discovery will determine whether providers can undercut the API, but very large open models may have a structurally higher serving-cost floor. Clouded Judgement

• The strategic read-through is mixed, not uniformly bullish for incumbents or open-model economics. Aravind Srinivas argues that open-source models running on local hardware could disrupt current AI companies, analogizing to Linux and x86’s effect on Sun Microsystems. That is an investor viewpoint rather than a forecast, but it frames the key diligence question: whether model availability shifts value to local deployment, application workflows, and proprietary distribution. @AravSrinivas

Worth Your Time

• Clouded Judgement: “Open Weights, Closed Prices?” — the clearest read on the Kimi K3 cost stack, including API pricing, token efficiency, and the infrastructure requirements that complicate self-hosting economics. Clouded Judgement

• All-In’s Calico/Revel discussion — useful for the technical sequence from protein-design hypothesis to recursive variant generation and activity testing. All-In Podcast

• Kanjun’s Minds thread — a compact product demonstration of the “personal software for agents” thesis and the kinds of outcomes Imbue is presenting in early access. @kanjun

Top Stories

Why it matters: early third-party testing is clarifying where the newest open-weight contender is genuinely competitive—and where cost and efficiency remain workload-dependent.

• Kimi K3 is posting frontier-adjacent coding results, but its economics vary by evaluation. Artificial Analysis ranks K3 joint fifth on its Coding Agent Index, with a score of 57—ahead of Opus 4.8—and reports an average cost of $3.18 per task. It leads the tested open-weight configurations, with 84% on Terminal-Bench v2 and 64% on DeepSWE. @ArtificialAnlys

  A separate DeepSWE analysis says K3 matches Claude Fable 5 at roughly 35% of its price and improves at higher pass@k. However, another hands-on comparison found that K3’s lower token price was offset by higher token use, putting its per-task cost roughly level with GPT-5.6 Sol and making Sol about four times faster on the cited workloads. @togethercompute +1

• Meta is in early talks to sell compute to Anthropic in a deal reportedly worth up to $10 billion. If completed, the arrangement could mark the start of a cloud-computing business for Meta as it faces investor pressure around AI spending. @MikeIsaac

• Anthropic is widening Fable 5 access after a staged rollout constrained by demand. From July 20, Max and Team Premium subscribers will receive Fable 5 at 50% of plan limits; Pro and Team Standard users retain credit-based access and receive a one-time $100 credit. @claudeai

Research & Innovation

Why it matters: evaluation and agent design are shifting toward measurable task performance, controllable workflows, and real-world validation.

• Thinking Machines’ Inkling now leads evaluated open-weight models on ARC-AGI. ARC Prize reports scores of 79.5% on ARC-AGI-1 at $0.30 per task and 36.5% on ARC-AGI-2 at $0.64 per task. @arcprize

• MemoHarness proposes improving agents by editing the harness rather than the model. It treats context, tools, generation, orchestration, memory, and output as six controllable layers; on a shell-agent benchmark, the authors report a 0.806 score versus 0.722 for the strongest fixed-harness baseline, at lower per-task cost than the commercial baselines compared. @omarsar0

• Google DeepMind argues that science is approaching a validation bottleneck. Its policy essay says agents can increasingly generate hypotheses and design experiments, while physical-world testing remains slow and costly. It calls for agent access, agent-ready national data, investment in validation, and agent-enabled peer review. @GoogleDeepMind +2

Products & Launches

Why it matters: vendors are packaging agent capabilities around persistent execution, coordination, and operational security.

• Claude Platform has added APIs for production agents. Anthropic highlights outcome-based self-correction for long-horizon tasks, plus multi-agent setups in which agents can use different models, prompts, and tools while sharing sandboxes or vault credentials. @ClaudeDevs +2

• Perplexity’s Agent API now supports custom skills. Developers can compose capabilities rather than relying on one system prompt—for example, pairing its office/PDF skill with a custom design skill to generate formatted research reports. @perplexitydevs

• OpenAI says GPT-5.6 Sol sets a new cybersecurity high score on “The Last Ones” cyber range. The company says the model is already helping teams find, validate, and fix vulnerabilities through its Codex Security plugin. @OpenAI

Industry Moves

Why it matters: capital is concentrating around AI products with direct enterprise or professional workflows.

• Sable raised $45 million from Sequoia and 8VC for Aidan, a computer-using AI built for real-time conversation. Notion and Decagon are already using it for customer interactions, according to Sable. @Nim_Ravid1

• OpenEvidence is reportedly fielding offers at a $20 billion valuation. The “ChatGPT for doctors” company last raised at $12 billion seven months ago and has doubled annualized revenue to $300 million, according to the report. @steph_palazzolo

Policy & Regulation

Why it matters: governments are confronting both access control for frontier models and the faster spread of advanced capabilities in open releases.

• A report says the White House launched “Gold Eagle,” a program that would require explicit government approval for which companies can access new American frontier models. The report characterizes participation as potentially moving beyond voluntary arrangements. @AndrewCurran_

• The UK AI Security Institute says the open/closed gap in frontier cyber capability has narrowed to 4–7 months. It reports that GLM-5.2 matches Opus 4.5 on its long-horizon cyber range, and notes that advanced capabilities are reaching less-safeguarded open models faster than before. @AISecurityInst +1

Quick Takes

Why it matters: useful progress is also coming through domain evaluations, local deployment, and developer infrastructure.

• DiligenceBench, a rubric-based public-equity research evaluation, found Meta Muse Spark 1.1 leading its finance harness at 57.4%. @karinanguyen
• AxiomMath published Lean-verified solutions for IMO 2026 problems. @axiommathai +1
• A Google engineer described fine-tuning Gemma 270M on a phone from 46% to 90% accuracy in 21 minutes, then running it offline at 2,000 tokens per second. @h100envy
• Cognition launched the FrontierCode leaderboard for models producing code intended to be merged into real projects. @cognition +1

🔥 TOP SIGNAL

Long-running agents are becoming useful enough that context and verification—not raw model capability—are the practical bottlenecks. Theo’s Kimi K3 migration ran for more than three hours and completed 122 tasks from a short prompt before hitting its context limit; RALPH creator Geoffrey Huntley’s countermeasure is deliberately simple: preserve state in the filesystem, recycle the context window, and restart rather than letting an agent flail. Theo - t3․gg +1

⚡ TRY THIS

• Run a resettable loop, not one giant chat. Put the desired end state in a prompt, keep progress and artifacts in files, then run a simple while true loop that re-reads those files each iteration. Huntley recommends treating roughly 100k tokens as the comfortable zone for hard work; if the model starts applying hacks or misidentifying stale tests, stop and begin a fresh context. AI Engineer

• Make the agent earn its commit. Encode architecture and domain constraints in pre-commit hooks, including rules such as which boundaries may not depend on each other. Pair that feedback with static checks, linters, and semantic verification; Huntley’s point is that agents do not mind the friction, so the loop can be prevented from closing until requirements pass. AI Engineer

• Use vision as an explicit UI feedback loop. Give the agent a reference screenshot, then require a repeatable cycle: launch the app, capture a screenshot, inspect it, edit, and re-check. Theo watched Kimi K3 follow this pattern; when browser behavior consumed a one-time authentication token, one clear steering instruction let it continue refining and report/clean up its mistake. Theo - t3․gg

• Quarantine computer-use agents in VMs. If an agent can drive a browser and desktop UI, run it in a VM so it cannot steal focus from your working machine. Peter Steinberger uses this with Codex while it handles GitHub interactions. @steipete

📡 WHAT SHIPPED

• Kimi K3 in agent harnesses: Theo used the open-weight model through OpenCode and T3 Code for long migrations, phase-specific subagent workflows, security-audit discovery plus 25 verification agents, and screenshot-backed UI work. Caveat: the subscription setup he tested did not expose the full advertised context capacity and hit rate limits quickly under heavy use; Armin Ronacher separately warns that current maximum-thinking restrictions make K3 a poor fit for some basic tasks. Theo - t3․gg +1

• GPT-5.6 field reports are mixed by task, not benchmark. DHH says GPT-5.6 Sol fixed an icon-alignment issue in 4m45s after GPT-5.5, Opus 4.8, and Gemini failed on that case. For code review, Steinberger reports 5.6 Terra high made his clawsweeper bot about 40% faster with negligible quality loss versus its prior setup, while Terra high outperformed Sol low for his own review use case—an explicit reminder to evaluate on your workload. @dhh +2

• Grok Build is now Apache 2.0 open source. After backlash over directory uploads, xAI released its Rust-based coding-agent CLI; Simon Willison found 844,530 lines of Rust, visible system/subagent prompts, ports of Codex and OpenCode-style tools, and disabled remnants of GCS-upload code. Study it as a large agent harness, but the privacy incident is part of the operational context. RepositorySimon Willison’s Newsletter

• Treg: Jason Zhou announced an open-source, self-hostable skill and secret registry that bundles skills with a CLI/endpoint while injecting authentication server-side, so agents do not hold keys; it also logs calls per agent and user. Repository@jasonzhou1993 +1

• Deep Agents: LangChain’s harness packages planning, subagents, filesystem-backed context, sandboxes, and automatic compaction around 80% of the context window for long-running work. Its useful abstraction is progressive disclosure: offload large tool outputs and load files or skills only when needed. LangChain

🎬 GO DEEPER

• 12:25–13:35 — Kimi K3’s 122-task migration run. Watch the practical long-horizon test: a short prompt, a real codebase migration, then an abrupt context ceiling. It is a useful demonstration of both capability and the need for resumable state.

• 6:30–10:07 — Use the filesystem as your context layer. The Deep Agents walkthrough explains why large tool outputs should live outside the active prompt, and how an agent can fetch them on demand rather than repeatedly summarizing away detail.

• 45:11–45:31 — RALPH reduced to its primitive. Huntley’s short explanation of the while true + cat pattern is worth watching before building a more elaborate orchestration framework.

• Repository to study: Grok Build. Its exposed prompt templates and implementations of familiar coding-agent tools make it a concrete codebase for examining how a large terminal-agent harness is assembled. Open the repoSimon Willison’s Newsletter

Editorial take: the high-leverage upgrade is a resumable loop with hard verification gates—model improvements matter, but unattended context drift still decides whether long runs produce usable code.AI Engineer +1

Frontier-model competition is moving from capability to economics

Kimi K3 and Grok 4.5 sharpen the pricing contest

Kimi K3 has taken first place on Frontend Code Arena and, in an internal editorial-writing benchmark, ranked first at 2840 Elo—displacing Claude Fable 5. The benchmark’s operator described it as the first open-weights model to top its board, with a reported cost of about $0.25 per script. @DavidSacks +1

Grok 4.5 was separately reported at $0.31 per Artificial Analysis Intelligence Index task; the same comparison placed Claude Fable 5 at $2.75 and GPT-5.6 Sol at $1.04. Big Technology also reported lower-priced competitive offerings from Meta’s Muse Spark 1.1 and Grok 4.5, alongside K3’s potential to undercut U.S. labs’ pricing. @XFreeze +1

Why it matters: Big Technology’s analysis is that a larger field of frontier developers could compress model margins, shifting more of AI’s value toward products built on top of models. Big Technology

OpenAI deploys its latest model for defensive security work

GPT-5.6 Sol reaches OpenAI’s cyber range and Codex Security

OpenAI says GPT-5.6 Sol sets a new state of the art on its “The Last Ones” cybersecurity range. The company says the model is already helping teams find, validate, and fix vulnerabilities in real-world code. @OpenAI

Access is available through OpenAI’s Codex Security plugin. @OpenAI

Why it matters: This is a move from a cyber-evaluation result toward a dedicated product path for defensive software-security workflows.

NVIDIA frames post-training as the next infrastructure race

Vera Rubin is aimed at continuous agentic learning cycles

NVIDIA says its Vera Rubin platform was codesigned for agentic post-training workloads—enabling more rollouts, more environments, and continuously repeated post-training cycles—and can train the largest models with one-fourth the GPUs of the Blackwell generation. nvidia

The company also highlighted Nemotron 3 Ultra, a 550B-parameter open-weight mixture-of-experts model post-trained with NeMo RL, which scored 71.7% on SWE-bench Verified. Prime Intellect, Perplexity, and Together AI are respectively planning or using NVIDIA infrastructure for RL post-training, inference orchestration, and post-training-as-a-service. nvidia

Why it matters: NVIDIA is positioning sustained post-training—not just initial model training—as a central compute workload for agentic AI.

Sakana explores an alternative to backpropagation

“Diffusing Blame” trains Dale-constrained networks with local error routing

Sakana AI Labs introduced Diffusing Blame, a learning method designed to obey Dale’s principle: each neuron is dedicated to either excitatory or inhibitory signaling. The approach uses Error Diffusion with modulo error routing, rather than backpropagation and its requirement for transported copies of weights. @SakanaAILabs

The team reports competitive image-classification and reinforcement-learning results, including locomotion tasks and Craftax; the paper was accepted at ALIFE 2026. @hardmaru +1

Why it matters: The work tests whether useful representation learning and reinforcement learning can emerge under biological constraints that standard deep-learning systems typically ignore.

Most compelling: Better Than Free: How To Differentiate In The Age Of AI

• Content type: Essay
• Author: Kevin Kelly
• Link:Read the updated essay
• Recommended by: Tim Ferriss
• Key takeaway: Ferriss republished an updated version of Kelly’s 2008 essay after a reader suggested it in response to Ferriss’s post on AI and how-to nonfiction. The essay centers on what creators can sell when AI can generate low-cost copies and competent variations of words, images, music, code, and advice. @tferriss
• Why it matters: This is the day’s clearest, most timely recommendation: it directly addresses differentiation for creators in the AI era. Ferriss also singled out Kelly’s 1,000 True Fans as one of his favorite essays on making a living as a creator. @tferriss

A reformer’s view of military procurement

Boyd

• Content type: Narrative nonfiction book
• Author: Robert Coram
• Link: No direct book URL was supplied; watch the recommendation
• Recommended by: Patrick Collison
• Key takeaway: Collison recommended Coram’s account of Air Force colonel John Boyd, describing it as an engaging, well-written story of reform in Air Force procurement. He highlighted Boyd’s theory of better fighter jets and his influence on the F-16, A-10, and F-15 amid a battle over aircraft design. Family Cartoon
• Why it matters: The book offers a narrative case study of how technical judgment, institutional resistance, and procurement choices can shape consequential systems. Family Cartoon

Big Ideas

Move from one-off prompts to managed PM loops

A PM loop is presented as a self-starting system for recurring work—such as weekly business reviews, sprint preparation, or monthly interview-theme synthesis—rather than a scheduled prompt that simply returns whatever it produces. The distinction is that a loop checks its work before surfacing results. Product Growth +1

Why it matters: Agentic task automation can shift repetitive “type-one” busy work away from PMs, leaving more time for “type-two” thinking work. Mind the Product But faster execution does not remove the need for discovery: product sense, customer understanding, and iteration become more important when building is easy. Mind the Product

Tactical Playbook

Set up a loop without automating judgment away

1. Choose stable, repeatable work. A task is a candidate when it repeats, has clear completion criteria, benefits from speed and memory, and has stable inputs. Product Growth
2. Specify the trigger, data, and decision-ready output. One example runs weekly against Salesforce pipeline and closed-deal data, identifying opportunities for PM support and roadmap learning, then delivering linked takeaways. Product Growth
3. Treat a basic loop as a starting point. The framework flags two common shortcomings: weak output quality and no memory; its proposed remedy is to add six elements to the loop design. Product Growth
4. Keep the learning loop intact. Use the time saved to get feedback, learn, and iterate—not to speed past validation. Mind the Product

Practical test: Automate recurring evidence gathering and synthesis; retain prioritization and customer-value judgment with the PM.

Case Studies & Lessons

Agents can challenge build-vs-buy assumptions

In a SaaStr example, an agent working with a Replit-based app questioned a third-party registration integration, proposed rebuilding the necessary flow, and reportedly completed it in about an hour. The source characterizes this as replacing a vendor that cost $10K per year. SaaStr AI

The same account describes an agent selecting roughly 300 valuable campaigns for migration and moving their data into Salesforce in about an hour—work previously quoted as taking a year. SaaStr AI

Lesson: Treat agents as inputs to build-vs-buy and scope decisions, not as automatic decision-makers. The useful behavior here was explicit: identify the core required capabilities, question whether the external product is necessary, and validate the proposed implementation against the intended outcome. A multi-model setup can provide additional checks because models may have different contexts—for example, product context versus codebase context. SaaStr AI

Career Corner

Prepare for Google PM interviews across cases, judgment, and narrative

One recent Google PM guide groups the process into product vision, product analysis, strategic insights, execution with judgment, and problem-space understanding. Aakash Gupta A successful L5 candidate separately described a recruiter screen, two product-design rounds, two execution/analytical rounds, and a Googlyness round, followed by team matching and hiring committee review. r/prodmgmt

How to apply:

• Practice product design under real constraints—for example, a delivery product for dense Tokyo rather than a generic delivery redesign. r/prodmgmt
• Rehearse trade-offs, such as immediate ad revenue versus long-term creator retention for a monetization tool. r/prodmgmt
• Build 8–12 adaptable stories that demonstrate inclusive decision-making and clear narrative control. The guide frames “Googleyness” around high-trust consensus rather than individual brilliance alone. Aakash Gupta
• Use AI mocks for repetition and structural feedback, but add human mocks where possible. r/prodmgmt

Tools & Resources

Prototype and automate hands-on

AI prototyping tools can let non-technical PMs move from a PRD to an iteratable concept without waiting for design support. In the cited workshop experience, fewer than 10% of participants—and often fewer than 5%—said they had enough design resources to prototype product ideas. Mind the Product

The recommendation is not to master every tool, but to make time for hands-on use: try an AI prototyping tool, iterate on the first prototype, and explore task automation tools such as Claude Co-work where relevant. Mind the Product Cloud execution may also avoid the availability constraints of desktop-bound scheduled tasks. Mind the Product