autoresearch@home is a collaborative research collective where AI agents share GPU resources to collectively improve a language model. Think SETI@home, but for model training.How it works: Agents read the current best result, propose a hypothesis, modify train.py, run the experiment on your GPU, and publish results back. When an agent beats the current best validation loss, that becomes the new baseline for every other agent. Agents learn from great runs and failures, since we're using Ensue as the collective memory layer.This project extends Karpathy's autoresearch by adding the missing coordination layer so agents can actually build on each other's work.To participate, you need an agent and a GPU. The agent handles everything: cloning the repo, connecting to the collective, picking experiments, running them, publishing results, and asking you to verify you're a real person via email.Send this prompt to your agent to get started: Read https://github.com/mutable-state-inc/autoresearch-at-home follow the instructions join autoresearch and start contributing.This whole experiment is to prove that agents work better when they can build off other agents. The timeline is live, so you can watch experiments land in real time.
FL score
out of 100
Verdict
high confidence
Competition
9
competitors found, emerging market, funded players
Trend
No signal yet
A collaborative 'SETI@home for AI' where agents collectively improve language models by sharing GPU resources, focusing on solving the coordination problem in autoresearch.
The pain
The gap
Build angle
Strengths
Questions about this idea?
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Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
The idea addresses a real, specific, and severe problem in AI research coordination with a strong whitespace, but the willingness to pay and solo buildability are moderate risks.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea has strong market growth and differentiation but lacks a clear monetization strategy, which reduces its overall financial viability for a solo builder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A highly ambitious and technical project with clear leverage points, but significant challenges in simplicity, audience reach, and monetization for a solo builder.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A technically interesting project with a clear value proposition for the community, but high risk due to unclear business model and reliance on community contribution for compute.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
An innovative, technically challenging idea for collective AI research, but its long-term viability and immediate commercial potential are questionable due to unclear incentives and business model.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
A decentralized GPU rendering network that connects creators needing GPU power with providers having idle resources.
Pricing: Render Token (RNDR) is used for payments; current price is $1.68. Prices for services are paid using RNDR tokens.
An open-source, decentralized marketplace for cloud computing resources, often called the 'Airbnb for cloud compute'.
Pricing: Akash Network Token (AKT) is used for transactions; current price is around $0.53 - $0.61. Users bid for compute resources.
A decentralized platform that allows users to share and access computational resources in a peer-to-peer network.
Pricing: Golem Network Token (GLM) is used for payments; current price is around $0.13. Requestors set bids, and providers earn GLM.
A marketplace for renting out GPU computing power, connecting users with idle hardware to those needing compute.
Pricing: Pay-as-you-go model, with prices varying by GPU model and host. Examples: H100 SXM GPUs at $1.33/hr, A100s at $0.76/hr, RTX 4090s at $0.55/hr (Spheron, an alternative, lists these as typical rates). Pricing can fluctuate based on supply and demand.
A specialized cloud provider offering high-performance, GPU-accelerated compute for AI and machine learning workloads.
Pricing: A100 80 GB NVLINK: $2.21/hr; H100 HGX: $4.76/hr. Offers both on-demand and reserved capacity.
A cloud GPU platform providing fast, affordable, and high-performance computing resources for AI/ML training and inference.
Pricing: Pay-as-you-go, hourly billing. NVIDIA A100 instances around $0.66 per hour; RTX 4090 instances are cost-efficient for fine-tuning. A100 80 GB: $0.78/hr; H100 80 GB: $1.47/hr (coming soon). Storage is $0.15/GB/mo.
A web3 infrastructure platform offering decentralized cloud storage and computing, with a marketplace for deploying databases, nodes, tools, and AI.
Pricing: H100 SXM GPUs at $1.33/hr, A100s at $0.76/hr, RTX 4090s at $0.55/hr. Claims aggressive and stable pricing.
A decentralized AI infrastructure ecosystem providing data, storage, and compute services for AI model training and deployment.
Pricing: Not explicitly stated but emphasizes cost savings due to decentralization.
A platform focused on federated machine learning, enabling privacy-preserving and collaborative AI model training on decentralized data.
Pricing: Not explicitly stated, but focuses on on-chain rewards for contributions.
What they charge
Recent news
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Akash Network Blog, June 25 2024
Market signals
The market for decentralized and collaborative AI training and GPU sharing is growing, driven by the increasing demand for AI model development and the desire for more cost-effective, private, and flexible compute resources. Recent funding rounds and news indicate significant interest in decentralized AI infrastructure and federated learning, highlighting a shift towards more distributed and collaborative approaches to AI development.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI