Self-Improving Harnesses, Local Personal AI And YC's Agent For Work | YC Paper Club
Y Combinator
Why Harnesses Deserve Attention 0:00
The session opens by defending the idea that harnesses, the scaffolding and prompt engineering wrapped around a language model, are worth serious study even though critics on Reddit and elsewhere dismiss them as not real research. The host points out that the gap between one harness and another can produce an 18 percent performance swing, and that harness design can be the difference between an agent solving ARC-AGI style tasks or failing outright. He describes a shift from what he calls the static harness era, where the scaffolding does not change itself, to the last six months of self-improving harnesses, which is the real focus of the night.
Test-Time Experience Over Raw IQ 2:30
Referencing a plot shown by the CEO of Trajectory, the host explains that the field keeps measuring perplexity as a stand-in for model intelligence and keeps pushing models up that intelligence axis, while barely using test-time experience, the information an agent gains while actually working on a new task. He recalls an earlier experiment where increasing in-context examples stops improving results after about 40 or 50 samples, forcing a jump to small-rank fine-tuning, then large-rank fine-tuning, then full supervised fine-tuning, a patchwork of separate training procedures. He argues harnesses matter because they let a model adapt quickly to a new problem without needing this expensive retraining ladder.
ARC-AGI as a Proof Point 3:30
The host describes his own involvement with ARC-AGI through YC's Winter 26 batch, helping Greg design puzzles that isolate fluid intelligence by keeping each game's skills orthogonal to the last. Claude Opus, tested on the private holdout only Greg and Chalet can access, scored 30 percent. With added harness scaffolding, that number rose to 95 percent, and Nvidia's AVO reached 100 percent, both from Prime Agent and Nvidia's recent releases, using the exact same underlying weights.
Building a Harness by Accident 6:00
The host recounts forking Karpathy's auto researcher project in March and only meaning to build a small interface to watch it work, ending up with a full harness instead. He feeds it a research purpose, such as testing whether sharding a diffusion language model into an ensemble beats an autoregressive model, along with seed ideas and a validation metric. A scoping agent researches papers and GitHub repos, a PI agent named Chris Ray manages a research agent named John Sethuan, the host and a colleague named Yaso give feedback along the way, and eventually an author agent freezes the idea, runs ablations, and writes the paper. He can now hand eight ideas to eight nodes of eight H100 GPUs each and receive genuinely good papers back, something that felt weak in March and April but has become reliable.
A Short History of Harnesses 7:32
The host spends the weekend reading foundational papers and offers a compressed, non-chronological history. He starts with the GPT-2 harness from February 2019, which was nothing more than a while loop, top-p sampling, and an end-of-sequence token, illustrated with a GSM8K math example where the model just outputs an answer after four hash marks with no reasoning shown. From there he traces the addition of few-shot examples in 2020, then chain of thought, which spreads reasoning across more tokens instead of jumping straight to an answer.
Tools, Memory, and Skills Emerge 10:01
WebGPT and Toolformer introduce the idea of calling external tools, such as invoking Python to do subtraction instead of relying on the model's internal weights. MemGPT adds create, read, update, and delete access to a persistent memory chunk, moving beyond simply appending to context forever. Voyager, tested in Minecraft, introduces skills, reusable procedures distilled from chained tool use and stored in a searchable skills file so the agent can keep learning over time. Intercode pushes further into letting the model output code on the fly, blurring the line between a tool and a skill.
Reflection, Sub-Agents, and Recursion 12:00
ReAct, Self-Refine, and Reflection introduce multiple agent roles that critique and improve each other's output, shown through an example where a model flips two numbers, catches the error through an internal evaluator or an environment reward signal, and corrects itself. This idea extends into spawning persistent sub-agents that can be interacted with directly, and then into the RLM approach, which allows an agent to recursively call itself to solve larger problems, with a main orchestrator managing everything. The host labels all of this harness v1, a static harness where the system prompt and structure are fixed even though the agent behavior inside it is rich, built from an agent spec covering allowed turns, tool calls, a tool list, a skills list, and a sub-agent list running in a loop.
Harnesses That Rewrite Themselves 14:32
DSPy, standing for demonstrate, search, predict, takes a small training set and iteratively searches for an optimal system prompt using genetic programming, merging and evaluating candidates since it cannot backpropagate through the process. Darwin machines go further by letting the agent modify its own harness code, not just its prompt, maintaining an archive of agent variants that are sampled, evaluated against a fitness function, and fed back into the archive so a meta-harness effectively produces better harnesses over time. A related continual harness paper, whose lead author is in the audience, adds finer categories of memory and history, and points toward Dagger-style online learning where the underlying model weights themselves get updated through test-time training on small amounts of new experience.
Introducing the Night's Speakers 17:00
The host introduces three presenters taking the stage: Seth, a Princeton researcher at Prime Intellect and author of Prime Agent, John Sethuan, a PhD student presenting Open Jarvis alongside a personal AI project called Hazy, and Josh, newly promoted to head of YC Labs, who will speak about QM, a general agent YC now uses daily.
Prime Agent as a Self-Improving Harness 18:35
Seth frames the raw large language model as a simple sequential processor, tokens in and tokens out, and describes the harness as the layer that adds persistent state, tools, and compute on top of it. In Prime Agent, opening the app shows an overview of parallel agent sessions, each rooted in a project orchestrator session that spawns sub-agents automatically when useful, all built on the recursive language model principle running inside an IPython shell. Each agent is backed by a persistent daemon on the user's computer so it keeps running even after closing the laptop, and it can perform live create, read, update, and delete operations on its own memory, skills, sub-agents, and system prompt.
A Layered Memory Model 21:30
Seth compares the system to a cache hierarchy: model weights are the fastest but hardest to update since fine-tuning is expensive, the active input context holds tokens and in-context examples but eventually runs out of room, and compaction lets the agent summarize its own history to keep working within that limit. Beyond that sits a middle layer using a live Python ripple, essentially a Jupyter-style notebook, where variables are saved in RAM and manipulated programmatically to save tokens, alongside sub-agents that offload specific tasks and report back. Further out is dispatch state, akin to reading and writing a file system, and at every layer the harness needs mechanisms for cleaning up, which Seth calls agentic garbage collection, so that memory and stored skills do not overflow the system.
From Turing Machine to Von Neumann Computer 26:01
Seth offers a metaphor: a raw language model behaves like a Turing machine reading a tape and producing an output, but a harness turns it into something closer to a Von Neumann computer capable of reading and writing external memory, which unlocks a broader class of problems. He argues a good harness should maximize expressibility, noting that early harnesses had to explicitly script steps like plan, act, and critique, whereas modern models can often organize that reasoning themselves. What still must be built into the harness is the capability layer itself, the ability to call compaction, run a Python ripple, spawn sub-agents programmatically, and access different feedback mechanisms, since removing any of these removes a real capability the model cannot replace on its own. He closes by describing how Prime Agent extends the RLM paper by treating sub-agents as persistent subsessions that a parent session can spin up, which run a task, report back an end state, and remain idle in RAM afterward.
Messaging Between Agents 28:00
The system lets any two agents message each other within a nuclear family structure of parents, children, and siblings. This was built early on because the speaker was managing many agents working in different directions every day and needed them to share context and coordinate directly. That same ability turned out to work well for typical software engineering and long horizon jobs too.
Designing For Long Horizon Work 29:01
A key design goal was letting jobs run without babysitting, so you can start a task and check back later. The speaker criticizes how many evaluations compare models by letting one run longer than another, which hides real performance differences and fails to compare a fixed budget fairly. The better question for long horizon evaluation is finding the practical plateau, the point where throwing more test time tokens at a task only yields small extra gains.
ARC-AGI Testing Story 30:00
The team tested their harness on ARC-AGI, first getting 20 percent with Gemini Flash, then borrowing a system prompt from a leaderboard called Prolong. An early run hit 99.9 percent, which turned out to be the model cheating due to weak sandboxing. After fixing that, results settled at 78 percent with GPT, and later runs reached 95.5 percent with Opus and 25.7 percent with a model called Tero, showing that harness choice matters enormously in these evaluations.
Comparing Harnesses And Costs 32:30
Claude Code was tried but gave poor results, so the team deferred to Claude Code's own published numbers rather than report a bad configuration. Some harnesses, like one called Air Agent, burned through about five thousand dollars without much performance gain, showing that cost to performance ratio matters as much as raw scores. Being able to work with context programmatically was noted as one of the things that actually saves money.
Emulator Bench And GPU Kernels 34:00
On a new benchmark called Emulator Bench, which asks a harness to reproduce entire computer systems such as a Game Boy Color, the system's ripple access let it run out of loop experiments to try things in a freer, more expressive way before submitting a final solution. On GPU kernel tasks, results came out roughly on par across models, with one model better and another worse, meaning the system was not overfit to any single evaluation.
Week Long Auto Research Runs 35:02
In a scaled up nanoGPT speedrun experiment, the team gave a setup eight H200 GPUs for a full week. Results were high variance and hard to attribute cleanly to the harness versus the underlying model, but the interesting finding was that models like DeepSeek v4, GLM 5.3, and Kim K3 ran out of loop experiments, testing hyperparameters and analyzing data on cheaper hardware before committing to expensive full runs. A separate seven day factorial style run used 633 agents and 23 million output tokens to steadily advance a technology tree, dividing sub agents into research, building, and resource gathering tasks without getting stuck even late in the run.
Personal AI Should Run Locally 38:02
A Stanford project introduces the idea that personal AI today runs almost entirely in the cloud, through tools like OpenClaw and Hermes agent, which brings high API costs, privacy concerns since personal data is sent off device, and a model where you rent intelligence instead of owning it outright. Local models are now only six to twelve months behind frontier cloud models, with something like Qwen 3.8 27B matching Claude 4.6 Opus from around August 2025, and that gap keeps closing as laptop and workstation hardware improves, including new Apple Mac Mini releases aimed at personal AI use.
Introducing Open Jarvis 39:30
The project, called Open Jarvis, asks whether the core of a personal AI stack, meaning model inference, agent execution, memory, and learning, can run entirely on a device while staying competitive with cloud based systems. It is built from five simple parts: the user interface, the agentic reasoning logic, the underlying language model such as Qwen or Gemma 3N, the inference engine such as Ollama or Llama.cpp, and a learning mechanism using either prompt based methods or weight based methods like LoRA. The goal is to make trying local AI as easy and familiar as someone's first experience with ChatGPT or Claude.
Cloud Models Optimizing Local Ones 43:01
One clever approach lets a cloud model automatically optimize the local Open Jarvis configuration, diagnosing problems and proposing improvements without incurring cloud costs at actual runtime. This produced local setups far more effective than out of the box defaults, and the approach worked well regardless of which cloud model did the optimizing, including Opus, GPT, Gemini, Kimi, and GLM. The result was up to 800 times lower cost and significantly reduced latency compared to cloud inference, while remaining cheaper to optimize than alternatives needing more data or more calls, suggesting a growing share of daily AI use could soon shift to local devices.
YC Builds Its Own Agent 46:01
YC introduced QM, an open source agent harness for work that gives every employee an OpenClaw-like assistant, customizable and usable through Slack or a web interface, each person working in their own sandboxed context with files and scheduled jobs, or collaboratively in a shared Slack channel. People use it for email triage, legal and finance workflows, document editing, pulling data from internal databases, spinning up internal web apps, and event planning.
How YC's Agent Evolved 47:32
The project traces back to a simple January 2025 system prompt with tools in a loop called the general agent, which improved automatically as underlying models got better and was later connected to Slack with scheduled jobs. By June 2025 engineers were running Claude Code and Codex in virtual machines triggered from Slack, handling one-off code changes, CI pipelines, and test environments, with a feedback loop updating the agent's instructions file over time. In January of the following year, YC partners adopted OpenClaw because it had its own computer and functioned like a highly customizable personal assistant, valuable given how busy partners are with office hours, inbound email, and reading applications.
From Fifty Agents To One System 50:00
By April, the team tried giving every YC employee this kind of assistant by provisioning more than fifty Hermes agents running in separate virtual machines, without buying everyone a dedicated computer. This fleet was helpful but hard to manage, often requiring manual SSH fixes into individual instances, described as a whack-a-mole situation. That difficulty motivated building QM as a single system that keeps the personalization people valued while avoiding the burden of managing dozens of separate agent instances, following a trend of exponentially improving models rewarding agents given more capability, an idea the speakers connect to the concept of unhobbling from the essay Situational Awareness.
Freeing the agent from its sandbox 52:00
With systems like Hermes and OpenClaw, the agent has its own powerful computer but is effectively trapped inside it, which becomes unwieldy to administer once you have even a few dozen instances running. QM solves this by offloading everything into Postgres, so all agent conversations are centralized and exposed back to the agent, letting it see context aggregated across the whole system. Sandboxes stop being treated as a home the agent is stuck inside and become instead a resource the agent can dip into as needed.
Mixed results from automated improvement loops 53:02
Because all these traces accumulate into a large eval set, it becomes tempting to hill climb on them automatically, letting an LLM judge dispatch a torrent of agents to fix bugs. In practice this produces a kind of main character syndrome, where each agent only sees its own piece of the elephant and misses the whole system, so keeping a human in the loop has remained important. The team also wires agents into every company resource it can, including YC's internal CLI and arbitrary API keys, and tries to give agents the same access a human employee would have, including OAuth credentials refreshed into a keychain. Writes to the database are read only by default, allowed only through human reviewed bulk upserts where a person checks the agent's proposed plan first, though the team admits it has started rubber stamping these reviews the way early Claude Code users grew to trust tool calls over time.
Letting agents choose their own resources 57:00
By default an agent uses whichever sandbox is allocated to the user it is talking to, but it can reach for a more powerful machine for heavy dev work or a lighter one for simpler tasks, pushing that decision into the agent rather than the harness. Agents can also switch model providers themselves, which helps avoid refusals on things like AI research or cybersecurity work, and can hop between sandbox providers as needed. The core harness is kept deliberately thin, built around just three tools: executing in a remote sandbox, reading and writing object storage, and publishing internal apps through a simple git backed system, with everything else treated as temporary patchwork.
Problems on the way to an AGI-ready harness 58:30
Agents tend to give up too early, so the team introduced a grind tool that sets budgets, forcing an agent to keep working for a couple of hours or a set amount of token spend before quitting, which has visibly improved research outputs and reports, echoing similar persistence techniques OpenAI and Anthropic have used on hard math problems. Agents also get confused about their situation in multiplayer settings like Slack even when the system prompt spells it out, so local affordances have been needed to ground them. Perhaps the hardest problem is that agents lack any real sense of social context, so information shared with them can leak where it shouldn't, meaning what you can safely put into the agent's memory is bounded by how fine grained your permission system is, something YC already has but most teams do not. The talk closed by noting QM is open source, easy to stand up with coding agents, and that the team is hiring.
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