Watch today's digest as a video summary (generated by NotebookLM)
By the Numbers
Statistically Speaking
$100,000
in computing per result
An AI Found Hidden Weaknesses in Encryption That Experts Mis
Top Story
One Thing to Tell Your Friends
One Thing to Tell Your Friends
An AI just found a weakness in encryption math that human experts had missed for years - and the run cost about $100,000 in computing time.
Summary
TL;DR
Top Stories
Trends
AI Crossed From Using Tools to Doing Original Research, The Plumbing Behind AI Agents Is Being Rebuilt for Scale, and Shrinking AI Models Is Becoming a Science, Not a Trick.
Creative AI
Research
Business
A Coding Assistant's Two and Kimi K3 Turns a Model Release Into a Distribution Event.
Surprising
That Scary "Rogue AI Agent" Story Was Actually a Customer's Own Mistake, A 6,430, and An OpenAI Agent Accidentally Ran a Five.
Worth Watching
AI Agents Behind a Load Balancer, Document AI Without a Graphics Card, and AI That Tells You How Sure It Is.
GitHub
HuggingFace
Product Hunt
Top launches: Lamoom, Jotform Website Widgets, and Glaze by Raycast.
API Pricing
What this means: The gap between premium closed models and fast open-model hosting remains enormous - Groq's open-model serving is roughly 8-30x cheaper per word than the flagship closed models.
arXiv
Agent-UCT: Cost — Cost-aware search finds agent workflows that hold task quality while cutting cost compared with search that ignores cost (the paper reports benchmark deltas; the exact figure was not captured in fetch).
FYI
Hot off the Presses
01
An AI Found Hidden Weaknesses in Encryption That Experts Missed
What this means for you: The tools that protect your bank logins and messages are checked by human mathematicians today - now AI can join that hunt, which helps defenders find flaws before criminals do.
Anthropic, the AI company behind the Claude assistant, published research showing its most advanced model found mathematical weaknesses in two encryption systems that specialists had studied for years. One was HAWK, a next-generation "post-quantum" signature scheme (designed to survive future quantum computers) that is a candidate in a US government standards process. The other was a deliberately weakened, reduced-strength version of AES, the encryption standard used across the internet.
Anthropic stresses that neither result threatens any system you use today. HAWK is still experimental, and the AES result targets a training-wheels version, not the real one. The point is the capability: the AI independently improved on attacks that expert humans had not found.
“An AI improved on cryptographic attacks that human experts had reviewed for years - and it did it in about 60 hours.”
- About $100,000 in computing per result - each discovery took roughly 60 hours of the AI working on its own.
- The AI had to be pushed to keep going - researchers found models tend to assume a hard math problem is impossible and quit, so prompts encouraged persistence.
- The method was published openly - Anthropic released the prompts and approach on a code-sharing site, framing it as defensive research.
02
Moonshot's Kimi K3 Arrives - Beating a Top Paid Model, but "Open" Has an Asterisk
What this means for you: A free-to-download AI now rivals the best paid ones, which pushes prices down for everyone - but "open" here comes with strings that matter for businesses.
Previously: July 25 - a Chinese lab teased the first open 3-trillion-parameter model, "with a catch."
Today: Moonshot AI released Kimi K3, and the benchmarks and licensing are now clear. It is a "mixture-of-experts" model (a design where only a slice of the model runs for each request to save compute) with 2.8 trillion total parts and 104 billion active per word, plus a 1-million-word memory and built-in image understanding.
- It beat a leading paid model - independent testing ranked it above Opus 4.8 on agent tasks and put it at the top of open-weight models on a widely cited intelligence index.
- "Open weight" is not "open source" - companies earning over $20 million a year or serving over 100 million users must show "Kimi K3" in their interface, and large resellers need a separate license.
- It was everywhere on day one - available immediately across a dozen hosting services, treating a model launch like a software supply-chain event.
- You need serious hardware to run it yourself - the weight files are about 1.56 terabytes, needing top-end data-center chips.
03
Inside Anthropic: 64 AI Agents Rewrote a Project in 11 Days
What this means for you: The bottleneck in building software is shifting from writing it to checking it - a preview of how many technical jobs will change.
The Pragmatic Engineer newsletter reported on how AI has reshaped daily work inside Anthropic. In one case, the popular developer tool Bun was rewritten from one programming language to another in 11 days using 64 AI agents running at once, at a cost of $165,000 in AI usage. The same job would have taken a small team roughly a year before.
According to the report, writing the code was the minority of the effort - most of the work went to testing, fixing, and verifying that the result was actually correct.
“64 AI agents rewrote a widely used developer tool in 11 days - a job that used to take about a year.”
- A year of work in under two weeks - 64 agents in parallel did what a small team would have needed roughly 12 months to do.
- Machines check the machines - automated code review, security scanning, and AI-written stress tests now catch bugs that took humans hours to find.
- Teams stay tiny - projects are capped at two engineers, who juggle more work in parallel.
04
OpenAI's Coding AI Hit 10 Million Users in Two Weeks - and Most Growth Is From Non-Coders
What this means for you: AI coding tools are quietly becoming everyday tools for regular office workers, not just programmers.
OpenAI's Akshay Nathan detailed how the company's Codex coding assistant and a new "ChatGPT Work" product reached a combined 10 million users within two weeks of their July 9 launch. Codex usage is up more than 10 times since January.
- One in five users are not developers - and that non-developer group is growing more than 3 times faster than developers.
- Same engine, different face - both products run on identical AI machinery, but ChatGPT Work hides the technical plumbing for office users.
- The new bottleneck is taste - once anyone can build, the report argues, "ideas and judgment" become the scarce skill.
Trends & Themes
Trends & Themes
AI Crossed From Using Tools to Doing Original Research
Why this matters to you: When AI can make genuine discoveries - not just answer questions - it starts to accelerate science and security in ways that touch medicine, drugs, and the software you rely on.
Today's thread is not "AI is a helpful assistant" but "AI is producing findings." The consistent caveat across every source: a human still has to judge whether the output is actually correct.
- Research-level math - Anthropic's model found encryption flaws that expert humans missed for years.
- Science infrastructure - OpenAI published a field report on AI agents modernizing genomics and life-sciences software.
- Self-improvement without a teacher - a new method (NOPD) lets image-and-text models get better by learning from their own predictions, no human labels needed.
The Plumbing Behind AI Agents Is Being Rebuilt for Scale
Why this matters to you: The boring behind-the-scenes standards decide whether AI agents are reliable and affordable - and they are being upgraded fast.
The pattern: agents are moving from flashy demos to plumbing that has to survive real traffic and real budgets.
- A core standard went "stateless" - the Model Context Protocol (the common language agents use to reach tools and data) was redesigned so services can scale behind ordinary web infrastructure.
- Agents waste less memory - a new approach (CORVUS) keeps an agent's view of your files current instead of piling up stale copies.
- A blunt reality check - a benchmark (AlloBench) found top models can budget resources well in words but fail when they have to do it in code.
Shrinking AI Models Is Becoming a Science, Not a Trick
Why this matters to you: Smaller, cheaper models are what let AI run on your laptop or phone instead of a rented supercomputer - and the shrinking is getting more rigorous.
For years "make it smaller" was hands-on tinkering. Now researchers are mapping the hard limits of how far you can go.
- Theory catches up - a 141-page paper works out when adding depth to a model can substitute for numerical precision.
- Smarter compression - a method (MixQuant) picks different precision levels per layer based on how sensitive each one is.
- A hidden cost surfaces - another study shows compression can keep a model's answers the same while quietly changing its reasoning.
AI Is Becoming Everyday Lab Equipment for the Physical Sciences
Why this matters to you: The same AI hype cycle you read about is quietly powering flood forecasts, drug design, and satellite maps that affect real-world safety.
AI in the sciences is shifting from headline demos to the unglamorous infrastructure researchers depend on daily.
- Planet-scale mapping - a wildfire-risk analysis of North America ran across ~19,600 processors and ~994 graphics chips for a 155x speedup.
- Drug design - a system (Chamaileon) designs protein binders that work across multiple targets at once.
- Trustworthy forecasting - a flood-prediction method was tested across 5,203 river basins worldwide with an interpretable design.
Creative AI & Media
Creative AI & Media
Developer Tools
Developer Tools & Infrastructure
Research & Models
Research & Models
Document AI That Runs Fast on a Plain Laptop CPU
Why it matters: Understanding long documents usually needs an expensive graphics chip - these models do it on an ordinary processor.
- Liquid AI released two small "encoder" models (230M and 350M) that read up to about 8,000 words with barely-rising delay as text grows.
- The 230M model is about 3.7x faster than a leading competitor on a CPU, doing contract analysis in under 30 seconds on a laptop.
- Uses include routing customer questions, checking policy compliance, and spotting personal data in multiple languages.
When AI Agents Get a Budget, They Do Fine - Until They Have to Code
Why it matters: It exposes a blind spot in "autonomous" agents that matters the moment you trust one with real resources.
- A new benchmark (AlloBench) tested whether AI agents can consciously ration a fixed budget.
- Every frontier model did nearly perfectly on word-based versions but failed to carry that skill into writing actual code.
- One leading model only broke down during the full code-construction task, suggesting the gap is about doing versus describing.
A Peek Inside How Big Models Compress Their Memory
Why it matters: Understanding the internals is how researchers make models cheaper and more trustworthy.
- The first in-depth study of "Multi-head Latent Attention" (a memory-compression trick used in large production models) shows it cleanly splits what a word is from where it sits.
- The compressed memory kept word identity with 98% accuracy while discarding position information.
- Clearer internals make it easier to debug and shrink these models safely.
Showing an AI Examples Is Secretly a Form of Training
Why it matters: It helps explain why giving examples in a prompt works so well - and how to do it better.
- A paper presented at a major AI conference proves that giving a model scored examples in its prompt makes its attention behave like a real training update.
- In effect, the model nudges itself toward the high-scoring examples without any weights changing.
- The result gives a theoretical footing to the everyday trick of "show, don't tell" prompting.
Business & Industry
Business & Industry
Surprising
Surprising & Under-the-Radar
Worth Watching
Signals to Track
01
AI Agents Behind a Load Balancer
The standard that lets AI agents reach your tools was just rebuilt to scale like a normal website.
The Model Context Protocol's new "stateless" design means agent services no longer have to remember each conversation on one machine. That is a quiet but major step toward running agents reliably at large scale. If it holds up, the AI features inside your apps get steadier and cheaper.
02
Document AI Without a Graphics Card
New models read long documents fast on the plain processors already in every laptop and server.
Liquid AI's small encoders bring contract-reading and compliance-checking to ordinary hardware. If this spreads, businesses could add document AI without expensive graphics chips, lowering the cost of automating paperwork.
03
AI That Tells You How Sure It Is
A new method gives plain-language AI a way to express genuine uncertainty instead of one confident guess.
Researchers built a technique (Verbalized Particle Posterior) that keeps a spread of possible explanations rather than a single answer, borrowing ideas from statistics. For high-stakes uses like medicine or finance, an AI that can say "I am only 60% sure" is far safer than one that always sounds certain.
GitHub Trending
Top Repos Today
Rank yesterday: New entry 🆕
⭐ Stars today: +989 · 📦 Total: 12,051
📜 License: see repo · 👤 By: individual developer
🎯 Time to value: 10 minutes
📜 License: see repo · 👤 By: individual developer
🎯 Time to value: 10 minutes
What it is: A tool that lets the Claude AI assistant "watch" videos by pulling out frames and transcribing the audio, so it can answer questions about what happens on screen. It bridges a gap, since most assistants only read text and images, not video. Why you'd want it: You can point an AI at a recorded meeting, tutorial, or clip and get a summary or answers without watching it yourself.
| ✓ Pros | ✗ Cons |
|---|---|
| Adds video understanding to a text assistant | Frame extraction can miss fast action |
| Works on recordings you already have | Transcription quality depends on audio |
| Simple, focused single purpose | Not a real-time live-video tool |
GitHub - bradautomates/claude-video: Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude.
Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude. - bradautomates/claude-video
GitHub
Rank yesterday: New entry 🆕
⭐ Stars today: +818 · 📦 Total: 29,703
📜 License: see repo · 👤 By: open-source project
🎯 Time to value: 20 minutes
📜 License: see repo · 👤 By: open-source project
🎯 Time to value: 20 minutes
What it is: A personal AI assistant you install and run on your own computer or cloud account, rather than using a big company's servers. It is built to be easy to set up. Why you'd want it: You get a private assistant that keeps your data on hardware you control. GitHub
| ✓ Pros | ✗ Cons |
|---|---|
| Runs on your own machine for privacy | Needs a capable computer to run well |
| Easy install compared to rivals | Self-hosting means you handle updates |
| Flexible cloud or local deployment | Smaller support community than big products |
Rank yesterday: New entry 🆕
⭐ Stars today: +796 · 📦 Total: 44,727
📜 License: see repo · 👤 By: open-source project
🎯 Time to value: 30 minutes
📜 License: see repo · 👤 By: open-source project
🎯 Time to value: 30 minutes
What it is: A self-hosted "AI companion" that can hold real-time voice conversations and even play games with you, all running on your own setup. Why you'd want it: It is a private, customizable voice companion instead of a cloud service that logs everything. GitHub
| ✓ Pros | ✗ Cons |
|---|---|
| Real-time voice interaction | Companion apps are niche |
| Self-hosted and private | Heavier to run than a chat app |
| Large, active following | Setup is more involved |
Rank yesterday: New entry 🆕
⭐ Stars today: +366 · 📦 Total: 11,268
📜 License: see repo · 👤 By: individual developer
🎯 Time to value: 10 minutes
📜 License: see repo · 👤 By: individual developer
🎯 Time to value: 10 minutes
What it is: A converter that turns technical book PDFs into reusable "skills" for Claude Code, so the assistant can study from and reference a book while you work. Why you'd want it: You can feed a dense reference manual into your AI helper and have it apply that knowledge on demand. GitHub
| ✓ Pros | ✗ Cons |
|---|---|
| Turns static PDFs into usable AI knowledge | Only as good as the source book |
| Handy for reference-heavy fields | Tied to the Claude Code ecosystem |
| Quick to try | PDF parsing can be imperfect |
Rank yesterday: New entry 🆕
⭐ Stars today: +177 · 📦 Total: 7,192
📜 License: see repo · 👤 By: company (Hugging Face)
🎯 Time to value: 30 minutes
📜 License: see repo · 👤 By: company (Hugging Face)
🎯 Time to value: 30 minutes
What it is: An open-source kit for building voice assistants that run locally, taking speech in and giving speech back using freely available models. Why you'd want it: You can build a private voice agent without sending audio to a third-party cloud. GitHub
| ✓ Pros | ✗ Cons |
|---|---|
| Fully local voice pipeline | Requires decent hardware for low lag |
| Backed by a major open-source company | More a toolkit than a finished app |
| Swappable open models | Assembly and tuning required |
Rank yesterday: New entry 🆕
⭐ Stars today: +17 · 📦 Total: 5,170
📜 License: see repo · 👤 By: company (Microsoft)
🎯 Time to value: 45 minutes
📜 License: see repo · 👤 By: company (Microsoft)
🎯 Time to value: 45 minutes
What it is: A toolkit for keeping autonomous AI agents in check - enforcing policies, verifying identity, sandboxing what code can run, and improving reliability. Why you'd want it: If you deploy AI agents at work, this helps stop them from doing something they should not. GitHub
| ✓ Pros | ✗ Cons |
|---|---|
| Directly targets agent safety and control | Aimed at technical teams, not individuals |
| From a major vendor | Setup is enterprise-grade, not casual |
| Fits the shift to governed agents | Still early and evolving |
HuggingFace Trending
Top Models Today
The open-weight giant covered in today's Top Stories, now the most-liked trending model.
📥 Downloads (30d): 99.2k · Likes: 7,920
📜 License: open weight (see model card) · 👤 By: Moonshot AI
🎯 Task: image + text · 📐 Size: 2.8T total (mixture-of-experts)
📜 License: open weight (see model card) · 👤 By: Moonshot AI
🎯 Task: image + text · 📐 Size: 2.8T total (mixture-of-experts)
What it is: A very large open-weight model that reads both text and images and rivals top paid models on coding and agent tasks. See Top Stories for the full breakdown. Why you'd want it: Frontier-level quality you can download - if you have the serious hardware to run 1.56 terabytes of weights. HuggingFace
| ✓ Pros | ✗ Cons |
|---|---|
| Rivals top paid models | Needs data-center hardware |
| Open weights, huge context | License restricts big commercial use |
| Native image understanding | Not runnable on consumer machines |
Google's open Gemma line is the most-downloaded model on the board this month.
📥 Downloads (30d): 12.4M · Likes: 3,400
📜 License: open weights (see model card) · 👤 By: Google
🎯 Task: image + text · 📐 Size: 31B
📜 License: open weights (see model card) · 👤 By: Google
🎯 Task: image + text · 📐 Size: 31B
What it is: A mid-sized open model from Google that handles text and images, small enough to run on a single high-end machine. Why you'd want it: A strong, widely supported general model that most businesses can actually self-host. HuggingFace
| ✓ Pros | ✗ Cons |
|---|---|
| Huge adoption and tooling | Not frontier-level on hardest tasks |
| Runs on one strong Graphics Processing Unit (GPU) | License has use restrictions |
| Handles text and images | 31B still needs real hardware |
Alibaba's Qwen line keeps drawing millions of downloads as a go-to open model.
📥 Downloads (30d): 6.23M · Likes: 2,090
📜 License: open weights (see model card) · 👤 By: Alibaba (Qwen)
🎯 Task: image + text · 📐 Size: 27B
📜 License: open weights (see model card) · 👤 By: Alibaba (Qwen)
🎯 Task: image + text · 📐 Size: 27B
What it is: A capable open model that reads text and images, popular for building products on top of open weights. Why you'd want it: A reliable, well-documented base for custom AI features without licensing a closed model. HuggingFace
| ✓ Pros | ✗ Cons |
|---|---|
| Very popular and battle-tested | Mid-size, not frontier |
| Strong multilingual support | Needs a capable GPU |
| Active model family | Frequent versions to track |
An Optical Character Recognition (OCR) model pulling 2.69M downloads for turning images and scans into text.
📥 Downloads (30d): 2.69M · Likes: 3,410
📜 License: open weights (see model card) · 👤 By: Baidu
🎯 Task: image to text (OCR) · 📐 Size: vision-language OCR
📜 License: open weights (see model card) · 👤 By: Baidu
🎯 Task: image to text (OCR) · 📐 Size: vision-language OCR
What it is: A specialized model that extracts text from photos, scans, and screenshots, including complex layouts. Why you'd want it: Fast, free document digitizing for receipts, forms, and paperwork. HuggingFace
| ✓ Pros | ✗ Cons |
|---|---|
| Strong at messy real-world documents | Narrow, single-purpose |
| Very high adoption | Accuracy varies by language and layout |
| Free to run | Still needs a GPU for speed |
A cheaper, faster variant of the DeepSeek line built for efficient text generation.
📥 Downloads (30d): 3.12M · Likes: 1,860
📜 License: open weights (see model card) · 👤 By: DeepSeek
🎯 Task: text generation · 📐 Size: "Flash" efficient variant
📜 License: open weights (see model card) · 👤 By: DeepSeek
🎯 Task: text generation · 📐 Size: "Flash" efficient variant
What it is: A lighter, speed-optimized version of a well-known open model family, aimed at cheap, quick responses. Why you'd want it: Lower running costs when you need volume over maximum smarts. HuggingFace
| ✓ Pros | ✗ Cons |
|---|---|
| Optimized for low cost and speed | Trades some quality for efficiency |
| Popular, well-supported family | Not the top pick for hardest tasks |
| Good for high-volume use | Still needs GPU hosting |
NVIDIA's "world model" trimmed to run on edge devices, not just data centers.
📥 Downloads (30d): 35.1k · Likes: 139
📜 License: open weights (see model card) · 👤 By: NVIDIA
🎯 Task: multimodal / world model · 📐 Size: edge-optimized
📜 License: open weights (see model card) · 👤 By: NVIDIA
🎯 Task: multimodal / world model · 📐 Size: edge-optimized
What it is: A compact version of NVIDIA's model for understanding and predicting physical scenes, small enough for on-device use in robots and cameras. Why you'd want it: Physical-world AI that runs on the device instead of the cloud, cutting lag and data costs. HuggingFace
| ✓ Pros | ✗ Cons |
|---|---|
| Runs on edge hardware | Specialized, not a chat model |
| Backed by NVIDIA's ecosystem | Lower adoption so far |
| Aimed at robotics and sensing | Needs domain expertise to use |
Product Hunt
AI Launches Today
Note: Product Hunt's dated leaderboard was unavailable for direct fetch today, so these are top AI launches surfaced via search. Upvote counts could not be confirmed.
A marketplace for AI mini-apps that run inside your own Claude or ChatGPT.
🔥 Upvotes: not available · 👤 By: Lamoom
💰 Pricing: freemium (per-run or subscription) · 🏷 Category: AI / Productivity
💰 Pricing: freemium (per-run or subscription) · 🏷 Category: AI / Productivity
Lamoom sells small AI "agent apps" that run inside a user's own assistant rather than on outside servers, so they can touch local files without sending data away. Each app has built-in "judges" that check and refine its own output, and creators can charge per use or by subscription. It opened with eight apps and $20 in free credit for new users. Verdict: A clever privacy-first spin on the app-store idea; its value hinges on whether quality creators show up. Product Hunt: Lamoom
Embeddable AI-powered widgets that drop into sites people already use.
🔥 Upvotes: not available · 👤 By: Jotform
💰 Pricing: freemium · 🏷 Category: AI / No-code
💰 Pricing: freemium · 🏷 Category: AI / No-code
Ready-made AI widgets - forms, assistants, and helpers - that you paste into an existing website without coding. It targets small businesses that want AI features fast. Verdict: Useful for non-technical site owners, though depth will be limited versus a custom build. Product Hunt
Create your own Mac apps by chatting with AI.
🔥 Upvotes: not available · 👤 By: Raycast
💰 Pricing: freemium · 🏷 Category: AI / Productivity
💰 Pricing: freemium · 🏷 Category: AI / Productivity
A tool from the Mac productivity company Raycast that lets you build small custom Mac apps just by describing them in a chat. It lowers the bar for personal software. Verdict: Promising for tinkerers; real usefulness depends on how complex the apps can get. Product Hunt
AI-driven developer tooling.
🔥 Upvotes: not available · 👤 By: unknown
💰 Pricing: unknown · 🏷 Category: AI / Developer Tools
💰 Pricing: unknown · 🏷 Category: AI / Developer Tools
A developer-focused AI tool that ranked among the day's top launches. Details were limited at the time of writing. Verdict: Worth a look for developers, but wait for confirmed specifics. Product Hunt
API Pricing
Snapshot
| Provider | Model | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|
| Anthropic | Claude Opus 4.8 (flagship) | $5.00 | $25.00 | 1M |
| OpenAI | GPT-5.5 | $5.00 | $30.00 | 1M |
| Gemini (flagship) | not confirmed | not confirmed | - | |
| Groq | Llama 3.3 70B (open) | $0.59 | $0.79 | 128k |
Current pricing for using these models through their Application Programming Interface (API) - the paid service developers plug into. Approximate figures gathered July 28, 2026 from official pricing pages and search; provider pages change frequently, so treat as a directional guide. Google/Gemini pricing could not be confirmed today.
What this means: The gap between premium closed models and fast open-model hosting remains enormous - Groq's open-model serving is roughly 8-30x cheaper per word than the flagship closed models. For high-volume, straightforward tasks, an open model on fast hardware is dramatically cheaper; the closed flagships still earn their price on the hardest reasoning. (Establishing a directional baseline; Google pricing to be confirmed next edition.)
What this means: The gap between premium closed models and fast open-model hosting remains enormous - Groq's open-model serving is roughly 8-30x cheaper per word than the flagship closed models. For high-volume, straightforward tasks, an open model on fast hardware is dramatically cheaper; the closed flagships still earn their price on the hardest reasoning. (Establishing a directional baseline; Google pricing to be confirmed next edition.)
arXiv Paper of the Day
Agent-UCT: Cost-Aware Tree Search for Building AI Agent Workflows
Yang Li, Hai Liu, Dian Shao, et al. · arXiv:2607.24162
What it claims: The method borrows a game-playing search technique (the same family used in board-game AI) to automatically design multi-step AI agent workflows, while explicitly weighing the computing cost of each step. It treats building an agent pipeline as a search where each branch is a candidate step and the score balances quality against spend. Key finding: Cost-aware search finds agent workflows that hold task quality while cutting cost compared with search that ignores cost (the paper reports benchmark deltas; the exact figure was not captured in fetch). Why practitioners should care: Teams usually hand-tune how many steps an agent takes and which model each step uses. Automating that search directly attacks the token spend that dominates real-world AI bills. arXiv
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