Your intelligence agent for what matters

Funding & Deals

• Campfire recently raised a Series B led by Accel and Ribbit Capital. The thesis is an AI-native ERP for high-growth tech companies that automates accounting, taxes, and investor reporting; YC also said Campfire has more than doubled ARR each quarter since Q4 2024 and now has 100+ employees after closing a $35 million Series A in June 2025 with 12 people@ycombinator.
• Conifer says it has funding to build an open-source local inference runtime for Apple Silicon. The five-person Princeton team is building it in Rust with handwritten kernels, says it is ahead of llama/mlx on small models, and is using a 100-user free beta to surface bugs and tool needs r/artificial.

Emerging Teams

• BioStack is the strongest early traction signal in the set. It builds simulation environments where healthcare AI models practice on real clinical data, converting messy records, lab tests, notes, and long-horizon outcomes into data, evals, rewards, and benchmarks; YC said revenue moved from six figures to seven figures in just the last few weeks@ycombinator. YC identified the founders as @sanatmishra7 and @patwa_parth@ycombinator.
• Callab_AI is attacking a large legacy-integration wedge. The company connects AI voice agents directly to on-prem PBX systems such as Cisco UCM and Mitel, avoiding migration in a market where 58% of the $400B call center industry still runs on-prem @ycombinator. YC identified the founders as @haithemkchaou and @chehir_dh@ycombinator.
• Mount is notable because it turns AI-agent risk into an insurable product. Its pitch is to secure autonomous-agent workflows, measure residual risk, and transfer that risk through insurance built specifically for AI agents so companies can use agents without carrying the full downside alone @ycombinator. YC identified the founders as @johnbachm and @fabeamherd@ycombinator.

AI & Tech Breakthroughs

• Delta Attention Residuals is the clearest research signal in this batch. Instead of routing over cumulative hidden states, it routes over deltas, which the authors say avoids routing collapse in deep layers and produces 1.8x sharper cross-layer routingr/MachineLearning. Reported results include 1.7-8.2% lower validation PPL from 220M to 7.6B, drop-in fine-tuning of pretrained models that beats baseline on 8 benchmarks, and 0.008% parameter overhead at 8Br/MachineLearning.
• Small local models are getting more practical. Garry Tan said Qwen2.5-7B Instruct is at GPT-3.5-turbo level and argued that even if local models are not the default, every device will need one as a fallback when connectivity fails @garrytan +1. Conifer is building toward that future with a runtime for fully local agents that can access files and apps under OS kernel enforcementr/artificial.
• AI-guided gene editing remains a frontier category. Nathan Benaich highlighted ProfluentBio's work on designing large gene insertions and fine-scale editing with AI@nathanbenaich.

Market Signals

• Verification, governance, and risk transfer are emerging as a distinct AI layer. Vinod Khosla called autoformalization the next critical frontier and said founders should work on areas where AI is weak @sathyanellore +1. In the same direction, Orygent is building a governed enterprise work layer around trust, approvals, audit trails, role-based authority, and verifiable AI, while Mount focuses on security plus insurance for autonomous agents @34marcopascha +2.
• Local inference is shifting from niche feature to resilience layer. Garry Tan said local models will not be the default, but every device will need one as an emergency generator when connectivity drops @harjtaggar. Conifer's funding and beta around Apple Silicon local inference is one startup expression of that view r/artificial.
• The software labor debate is becoming more explicit. Bindu Reddy argued that engineers are producing 10-100x more code, that layoffs will continue at companies with large engineering teams, and that the current status quo is unsustainable because of resulting instability in large codebases and teams @bindureddy.

Worth Your Time

• Delta Attention Residuals:paper and code. The work reports 1.7-8.2% lower validation PPL from 220M to 7.6B with 0.008% parameter overhead at 8Br/MachineLearning.
• World-model explainer:drops.mts.now/world-model. It covers what world models are, how they work, and what DreamZero and Agora-1 are building; Marc Andreessen amplified it on X @MTSlive +1.
• Campfire founder thread:X post. YC says the discussion covers launching the first paying version as a Google Sheet, pulling customers off NetSuite with four employees, and founder-led sales through Series A@ycombinator.
• BioStack launch page:YC launch. BioStack says it turns messy clinical data into post-training loops for healthcare AI, and YC says revenue moved from six figures to seven figures in weeks @ycombinator.
• Conifer beta and feedback:site and waitlist. The team says it is building an open-source Apple Silicon runtime and is taking 100 users into a free beta r/artificial.

🔥 TOP SIGNAL

• The interface is moving from code generation to goal ownership. Romain Huet says Codex can take a single ambitious /slashgoal and run on it for 3-5+ days — Peter Steinberger reportedly let one run for over a week — while the human mostly reviews diffs, comments inline, watches CI, and uses PR review as the control point. Simon Willison's red/green TDD advice is the matching guardrail: keep a test suite that stops agents from breaking old features while they make new changes. Hexa +1

• Huet also says more than 40% of tasks sent to Codex are non-coding automations, a sign that practitioners are already using these tools for broader work than code generation alone. Hexa

⚡ TRY THIS

• Run one big goal, then review at the diff/CI layer. Give Codex a single ambitious objective — e.g. update a neglected legacy codebase and keep going until the goal is reached — and let it work uninterrupted. Then review inline diffs/comments, create the Git commit/push, monitor CI in the app, and turn on Codex Code Review for the PR. Keep a red/green test suite in place so the agent can add new behavior without silently breaking old features. Hexa +1

• Use an image-first frontend loop. Ask Codex to generate a design image, iterate on the static result first, then tell GPT-5.5 to implement it. Use the in-app browser to point at elements and give concrete edits like change this font, make it larger, or move this to the right. Hexa

• Trim skill bloat and trust tool-based recall. Peter Steinberger's instruction when writing skills: tell the agent to be token efficient and relax grammar, because long skill descriptions get loaded into every context. Then run skill-cleaner to find the worst offenders, and let the agent recover forgotten details via tools instead of preloading huge slabs of context — Huet says GPT-5.5 can compact understanding and recall details later when needed. @steipete +1

• Use read-only audits before cleanup. KingBootoshi's Codex prompt was do a FULL read only analysis on my Macbook to help me optimize storage. Codex reportedly surfaced 500 GB of potential savings and a 116 GB codex-tui.log file; the replicable part is the first pass being explicitly read-only. @KingBootoshi

📡 WHAT SHIPPED

• Symfony — experimental spec + reference implementation for fleet-of-agents orchestration. Huet frames it as a world where you care about task completion, not staring at code: use Linear as source of truth and monitor which agents are executing or completing each task. Hexa

• Codex stack details — Huet says the Codex CLI and harness are open source; the harness defines tools, environment, file access, internet access, and MCP servers, and is also part of post-training so the model already knows its tool setup. Adoption signal: he says Peter Steinberger's output doubled after switching to Codex, and he completed 40 open-source projects in one year. Hexa

• skill-cleaner — Peter Steinberger released a skill to detect overly long skill descriptions. Repo: skill-cleaner SKILL.md. @steipete

• GPT-5.5 field signal from DHH.

  "All steering, no handwriting." @dhh

  DHH says GPT-5.5 has produced more "I can't believe it's this good" coding moments than any model since Opus 4.5, and says it fixed Omarchy 4 Alpha's busted webcam selector in under 2 minutes. @dhh +1

• Chorus/iMessage build flow — Riley Brown shared a path to go to http://chorus.com, add Claude to iMessage, and ask it to build an iOS app; he says the method works with any agent. @rileybrown

🎬 GO DEEPER

• 11:17-12:51 — Image-first frontend loop. Best short clip today for anyone building UI with agents: static design first, live browser second, implementation third. Hexa

• 13:48-15:00 — Codex as a workbench, not just a code generator. Inline diff review, Git, CI, PR review, plus the note that 40% of tasks are already non-coding. Hexa

• 18:04-19:15 — Why raw context-window talk is fading. Huet's point: agentic compaction plus tool recall matter more than stuffing everything into the prompt upfront. Hexa

• 24:30-25:09 — Symfony orchestration pattern. Very short clip, but the idea is durable: Linear as source of truth, multiple agents executing tasks, human supervising outcomes. Hexa

• Read: Simon Willison's red/green TDD for agent code — simple rule, high leverage. @simonw

• Read: Peter Steinberger's skill-cleaner SKILL.md — good example of using a skill to police other skills. @steipete

Editorial take: the durable edge today is ambitious async goals plus tight guardrails — tests, lean skills, and review/CI checkpoints — not longer prompts.Hexa +2

Top Stories

Why it matters: the biggest developments today point to three leverage points in AI: better agent scaffolding, alternative chip-scaling paths, and faster consumer app creation.

• Microsoft Research’s SkillOpt showed large agent gains without changing model weights. It treats the skill document as trainable external state for a frozen agent, with an optimizer model making validation-gated add/delete/replace edits. Microsoft said it was best or tied on all 52 tested model/benchmark/harness cells; on GPT-5.5 it added 23.5 points in direct chat, 24.8 with Codex, and 19.1 with Claude Code, with zero extra inference-time cost and transfer across models and harnesses @omarsar0. Another summary reported spreadsheet solving rose from 41.8% to 80.7% @TheTuringPost.

• Huawei used IEEE ISCAS to argue for a new semiconductor metric: τ-scaling. The framework shifts focus from transistor geometry to time-based optimization across devices, chips, and systems @kimmonismus +1. In its paper, Huawei says LogicFolding on Kirin 2026 can raise density from 155 to 238 MTr/mm², improve energy efficiency by 41%, and increase frequency by 13%, with a roadmap to 400+ MTr/mm² and "equivalent 1.4nm" density by 2031; Kirin chips using the new architecture are slated to ship this fall @ZhihuFrontier +1.

• Google AI Studio’s Android builder is scaling beyond developers. Google said users created more than 250,000 native Android apps in the first week after launch, and likely over 99% of creators had never built an Android app before @OfficialLoganK. That is a strong signal that prompt-driven software creation is already finding mass-market demand.

Research & Innovation

Why it matters: outside the headline stories, the most important research updates were about making models smaller, faster, and better at long-context memory.

• Gated DeltaNet-2 improved linear attention by separating erase and write operations. The 1.3B model reportedly beat Mamba-3 and KDA head-to-head on language, reasoning, and retrieval, with S-NIAH-3 rising from 63 to 90 @ahatamiz1 +1.

• MiniCPM5-1B pushed the small-model frontier forward. OpenBMB called it the strongest open-source base model under 2B parameters; it ranked #1 on Artificial Analysis’ small-model index at 17.9, ahead of Qwen3.5-2B at 16.3, and its ~0.5GB INT4 weights are designed for fully offline use on phones, browsers, and laptops @OpenBMB.

Products & Launches

Why it matters: launches were concentrated around developer tooling, cheaper inference, and image-generation workflows.

• xAI launched Grok Build, a coding CLI powered by Grok 4.3 Heavy, with a 2M-token context window and 8 parallel subagents @dl_weekly.

• Alibaba added automatic implicit caching to Qwen3.7-Max. The feature activates with no setup and is positioned as faster and cheaper out of the box; users who need more deterministic hit rates can choose explicit caching @Alibaba_Qwen.

• NVIDIA released PiD, a super-resolution model that works directly from model latents to deliver 4x resolution for generated images, with support for FLUX.1, FLUX.2, and Z-Image @multimodalart.

Industry Moves

Why it matters: the business signal is splitting between aggressive commercialization and harder questions about enterprise ROI.

• DeepSeek is reportedly seeking roughly $7.35B in fresh funding as rising compute costs push the lab toward commercialization @theinformation. Separately, another report said DeepSeek cut model prices by 75% @bfmbusiness.

• Anthropic moved ahead of OpenAI in Ramp’s latest business-adoption index, 34.4% to 32.3%, but cost pressure is rising. The same discussion pointed to image-inclusive prompts becoming 3x more expensive and said the fastest-growing vendors on Ramp are inference platforms selling cheap open-source models @kimmonismus. In a separate enterprise datapoint, Uber said the link between AI consumption and shipped features is "not there yet" after burning through its 2026 Claude Code budget in four months, while slowing hiring to fund AI spend @HedgieMarkets.

Policy & Regulation

Why it matters: legal pressure is widening from companies to individuals, while governments are becoming more explicit about AI sovereignty.

• Two authors sued individual researchers over training-data practices, alleging Guillaume Lample torrented 70TB of pirated books to help train Llama and naming former Meta AI executive Joelle Pineau as involved @ednewtonrex.

• Japan’s prime minister included domestic AI in a roundtable on the country’s "New Technology Nation" strategy. Sakana AI said it discussed industry-specific AI deployments plus ways to use overseas models while preserving Japan’s defense autonomy and data sovereignty through domestic technology; the prime minister described domestic AI as one of 17 strategic fields where startups are opening new paths @SakanaAILabs +1.

Quick Takes

Why it matters: a few smaller updates sharpened the picture on frontier reasoning, ethics, and robotics.

• DeepMind follow-up posts said Gemini plus agentic loops has now solved 11 major open math problems, while Demis Hassabis said today’s systems are still "nowhere near" AGI @LeiYu63 +1.
• Pope XIV said the Church and Anthropic will work together to "find the way for humanity" in the age of AI @disclosetv.
• LimX Dynamics opened global pre-orders for Luna, a 160cm commercial humanoid priced at RMB 298,000 in China, with claimed support for 200-unit fleet synchronization @humanoidsdaily.
• A deployment-aware context-optimization paper reported roughly 25% token savings at equal F1 and more than 50% lower token cost in high-performance settings on 5,000 HotpotQA instances @dair_ai.

AI moved closer to real workflows

The common thread today was AI moving out of demo mode and further into usable workflows: building mobile apps, assisting scientific research, and changing how teams think about software. What stayed unsettled was the payoff.

Google’s Android app builder is showing unusually broad early pull

Google said people can now build native Android apps directly in Google AI Studio for free, with no coding required, and Logan Kilpatrick said more than 250,000 apps were created since launch last week. He added that likely more than 99% of those creators had never built an Android app before, and pointed users to ai.studio/build as the entry point for reaching Android’s 3 billion active users @OfficialLoganK +1.

Why it matters: The notable signal here is not just another AI coding interface. Google is positioning app creation as a mass-market workflow, with early usage skewing heavily toward first-time builders @OfficialLoganK +1.

DeepMind is widening its research agenda while insisting AGI is still far away

Hassabis drew a distinction between solving well-defined problems with large amounts of compute and genuine invention of new objects, dimensions, or problems @ValerioCapraro. In parallel, DeepMind described a Gemini-based Co-scientist for hypothesis generation, data analysis, and literature summarization; earlier related systems that improved matrix multiplication and computer science algorithms; experiments with agent roles in a game environment; an automated materials lab in London; and a multi-model effort across the drug-discovery pipeline that is now in pre-clinical work Two Minute Papers.

Why it matters: This is a clear read on frontier-lab direction. DeepMind is pushing AI into research assistance, simulation, and lab automation while still arguing that current systems have not crossed into open-ended invention @ValerioCapraro +1.

The work narrative is splitting

AI-native operating models are getting clearer, but ROI skepticism is not going away

Dan Shipper argued that work increasingly happens inside agents such as Codex or Claude Code, with SaaS tools accessed through the agent’s in-app browser so the model keeps full context. He also said every automation still needs a human, and that the more durable organizational pattern may be a shared company “super-agent” maintained by a forward-deployed engineer rather than one personal agent per employee @lennysan. Chollet made a similar framing shift, arguing that AI should be seen less as a productivity booster for old workflows and more as a tool for doing new things in new ways, while LeCun said AI should amplify human intelligence and turn more engineers into managers of AI-augmented virtual teams @fchollet +1.

Marcus pushed in the opposite direction. He warned that coding models can generate “slop” that hurts large companies, and separately said the AI bubble could pop if enough companies report the same outcome @GaryMarcus +1.

Why it matters: The conversation is moving past whether AI will affect work. The sharper questions now are how much human oversight remains necessary, what new org charts look like, and whether current deployments are translating into measurable gains @lennysan +1.

One research paper worth tracking

Delta Attention Residuals targets a common deep-model failure mode

A paper highlighted in r/MachineLearning introduced Delta Attention Residuals as a drop-in alternative to standard residual connections, routing across layer deltas rather than cumulative hidden states to avoid routing collapse in deep models r/MachineLearning. The authors reported up to 8.2% lower validation perplexity at 7.6B parameters, gains across 220M to 7.6B scale, and the ability to retrofit pretrained models with at most 0.01% parameter overhead; paper and code are public r/MachineLearning.

Why it matters: If replicated, this is the kind of architectural tweak that matters because it promises measurable gains without a large parameter tax or a full stack rewrite r/MachineLearning.

Also notable

• Elon Musk said xAI plans to open source its 0.5T model toward the end of the year, adding that it should still be useful @elonmusk.

What stood out

The strongest recommendation in this batch was a research paper on how to optimize agent skill files. Rather than offering a generic endorsement, the recommender pulled out a concrete operating model for self-editing loops: strict validation gates, bounded edits, compact skills, and verification as the real bottleneck @koylanai.

Most compelling recommendation

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

• Content type: Research paper @koylanai
• Author/creator: Not specified in the source
• Link/URL:https://arxiv.org/pdf/2605.23904@koylanai
• Who recommended it: @koylanai @koylanai
• Key takeaway: The paper treats markdown skill files as trainable parameters and argues that performance depends on a strict validation gate, small edit budgets of 4-8 edits per step, compact final skills around a ~920-token median, and strong verification loops @koylanai
• Why it matters: This recommendation is unusually useful because it translates "self-improving agents" into specific design choices teams can apply when using agents to edit skills, prompts, or documentation @koylanai

Why this recommendation was high-signal

Two parts of the write-up made it especially practical:

1. It centers acceptance criteria, not just generation. The recommender said the validation gate is the decisive mechanism in a self-editing loop, using a held-out set, strict improvement, and rejected ties; in the paper's setup, the best skills ended with only 1 to 4 accepted edits total @koylanai
2. It argues for controlled, portable skills. The post says full rewrites hurt performance, 4-8 edits per step is the sweet spot, and procedural knowledge can transfer across runtimes. It also highlights a protected-section mechanism that keeps fast edits from overwriting slower, durable lessons @koylanai

Bottom line

If you work on agents, this paper stands out because the recommendation comes with concrete implementation heuristics instead of vague praise: keep edits small, keep skills dense, and treat verification as the hard problem @koylanai

Big Ideas

• Harness engineering is becoming PM work. At OpenAI, PMs shipped around 100K lines of production code through PRDs, tests, docs, and harness rules instead of typing in an IDE Product Growth. The harness combines an agents.md operating loop, a docs tree, tests and lints that encode taste, specialized review agents, and observability/computer-use checks Product Growth. In one experiment, an internal app reached roughly 1M lines of code from an empty repo with no human-typed production code; failures were addressed by improving the harness Product Growth. Why it matters: PM leverage is shifting from handoffs to executable artifacts. Apply it: write PRD + acceptance tests/evals + decision docs before implementation.

• Strategy still starts with the first user. Paul Graham argues startup ideas should be framed as idea + early adopters; if you cannot say who will use the product when nobody else is, move on @paulg +1. Building something you want helps because you and your peers become that first cohort @paulg. Apply it: add an "early adopters" line to every concept doc.

• Agentic products need human control surfaces. The emerging pattern is software that humans and agents use together, with approval flows, summary inboxes, logs, and rollback—not just agent-only CLIs @lennysan. Apply it: specify the human checkpoints for every AI workflow.

Tactical Playbook

• A safer PM workflow for AI coding agents:

  1. Stress-test architecture against the PRD before coding r/ProductMgmt.
  2. Pull fresh docs and research first r/ProductMgmt.
  3. Split work into small components; quality reportedly drops past about 200K tokensr/ProductMgmt.
  4. Generate tests first so the model has a target r/ProductMgmt.
  5. Use plan mode, manual approvals, and frequent Git pushes r/ProductMgmt.
  6. Restart polluted sessions; store key decisions in markdown and keep context narrow r/ProductMgmt.

  Why it matters: these steps directly target common hallucination and context failures.

Case Studies & Lessons

• Onboarding keeps surfacing as the real bottleneck. For a workflow tool, the biggest growth lever was reducing time from sign-up to real value, especially first project setup r/startups. A separate B2B SaaS complaint shows the opposite pattern: users create a password, verify email, set up TOTP, and import a team, then later delete the password once SSO arrives r/ProductManagement. Lesson: design onboarding for the end state, not the temporary workaround.

• In marketplaces, vendor onboarding/catalog ingestion can be harder than vendor acquisition. One founder said vendors liked the internal software once onboarded for orders and inventory, but uploading products, pricing, photos, descriptions, variants, delivery constraints, and categories could still take weeks r/startups. Options under consideration included concierge onboarding, internal tooling, PIM tools, POS integrations, and AI/OCR ingestion r/startups. Lesson: if setup takes weeks, acquisition is not the real growth constraint.

Career Corner

• AI-native PMs need executable specs. In one OpenAI example, a PM wrote a markdown PRD for a "skills system," the team reviewed it once, and the feature existed by week end with tests passing Product Growth. Practice: express one feature as PRD + tests + evals an agent can run Product Growth.

• For internships, signal structured thinking. Expect product improvement, prioritization, stakeholder conflict, metrics/KPIs, basic agile, and tech-fluency questions; interviewers are judging communication, curiosity, ownership, and problem solving r/ProductManagementJobs. Practice: answer aloud with a clear structure and success metric.

Tools & Resources

• Feedback tools still split into three jobs: analysis (Kapiche, Unwrap, Chattermill), collection (Canny, Productboard), and behavior (Pendo, Hotjar) r/ProductManagement. The main caution: separate collection and analysis tools force manual stitching across Slack, tickets, surveys, and widgets r/ProductManagement. A better selection principle is to favor a connected feedback graph so agents can surface patterns and PMs can spend more time on judgment Aakash Gupta.