Alternatives
Products that do what A playable toy model of frontier AI lab capex decisions does
I made a lightweight web game about compute CAPEX tradeoffs: https://darios-dilemma.up.railway.app/ No signup, runs on mobile/desktop. Loop per round: 1. choose compute capacity 2. forecast demand 3. allocate capacity between training and inference 4. random demand shock resolves outcome You can end profitable, cash constrained, or bankrupt depending on allocation + forecast error. Goal was to make the decision surface intuitive in 2–3 minutes per run. It’s a toy model and deliberately omits many real world factors. Note: this is based on what I learned after listening to…
- 1MB
2017 · moneyga.me
- 2IB
I built a web page that aggregates data about data center buildup, sovereign fund investments into AI and bottlenecks. The objective is to predict AI race cooldown by looking at a potential decrease of activity involving these elements. The website looks at the quarterly forms from the 5 biggest hyperscalers and adds their CapEx into the mix, calculating a composite index in the end showing how likely it is for the AI race to slow down. Enjoy!
Jul 2026 · laurentiugabriel.github.io
- 3TC
Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!
Jun 2026 · atomprophet.io
- 4NA
I've been tired with the current options on the market for awhile and decided to do something about it after the running into the disaster that is MLOps at my last two startups and having to manage a multiple operation platforms both for my fellow ML engineers, the general application CI/CD and orchestration layers while simultaneously building the application itself. Its still extremely early for the product but its functioning and is well on its way. I'd love feedback on the approach and peoples thoughts on the problem space. Personally my irritations have been in the poor tooling,…
2025 · egdaemon.com
- 5AS
I made a strategy game where you play the US or China through the AI race, 2026 to 2030, sixteen quarterly turns in the browser. One run takes about half an hour. At the start, the game seals two dice you never get to see. Inside: how hard alignment really is, and how fast takeoff compounds. You get eval reports, but only as ranges, and they flatter you most exactly when your systems are least aligned. At the end you get a debrief which shows what your evals said each quarter and also what was actually true. I lost every run I played myself so far. Every number in the game is source-backed…
Jul 2026 · criticalwindow.org
- 6IP
To be specific, the content is generated by a GPT-2 based model. https://amzn.to/2TCc0v2 Let me know if you have any questions :-)
2020
- 7OA
A friend and I are launching an alpha first thing in 2011. We're trying to gain some momentum and whatnot, so we're opening early registration as of tonight. We're planning to develop a Bayesian network to derive suggestions. If you're interested in seeing our progress, check it out. Edit: Any and all suggestions, criticism, advice, etc is highly appreciated!
2010 · osmoar.com
- 8IM
Also inspired by this HN submission: https://www.chiark.greenend.org.uk/~sgtatham/quasiblog/findl... The model is gpt-4o-mini-2024-07-18.
2024 · app4.hc11.org
- 9IB
I haven't seen anything like this so I decided to build it in a weekend. How it works: You see a bunch of things pulled from Wikipedia displayed on cards. You ask yes or no questions to figure out which card is the secret article. The AI model has access to the image and wiki text and it's own knowledge to answer your question. Happy to have my credits burned for the day but I'll probably have to make this paid at some point so enjoy. I found it's not easy to get cheap+fast+good responses but the tech is getting there. Most of the prompts are running through Groq infra or hitting a cache…
Apr 2026 · sleuththetruth.com
- 105L
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
- 11RA
Hi there, looking for feedback on my new project "Featherless.AI" The idea is to allow users to run all the models on hugging face instantly. Via the OpenAI API compatible endpoint. Why? Because its a real chore to download models and spin up GPUs, especially if you want to test multiple models. Not to mention GPUs cost multiple dollars an hour to rent. And if we want more people to use open source AI, we got to make it easier for them to try and play with all of them. So what if instead of spinning up dedicated GPUs per model (which is what every provider is doing) We can startup a LLM…
2024 · featherless.ai
- 12IR
Democratisation of local AI is key. I've been working on pushing the limits of commercial hardware, squeezing any extra bit possible. My Scientific Agentic AI hareness helped me to reallocate every single bit of it. I rewrote the Kernel, I went down the CUDA rabbit hole until I have been able to explain any bit and any ms of computational power involved in the process pushing the Qwen 30B-A3B from 8 tok7s to 19 tok/s with llama.cpp up to 22.2 tok/s with my project and 109 tok/s on not novel content and speeding up the prefill by 5-9X
Jul 2026 · github.com
- 13AH
This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…
2025
- 14MC
Hey HN - I built ModelGuessr, a game where you chat with a random AI model and try to guess which one it is. A big open question in AI is whether there's enough brand differentiation for AI companies to capture real profits. Will models end up commoditized like cloud compute, or differentiated like smartphones? I built ModelGuessr to test this. I think that people will struggle more than they expect. And the more model mix-ups there are, the more commodity-like these models probably are. If enough people play, I'll publish some follow-up analyses on confusion patterns (which models get…
Dec 2025 · model-guessr.com
- 15WB
Hey HN, After GPT-3 created waves in the tech industry, a lot of AI tools were emerging and with that, some AI website builders But the results seemed way too generic to us. It felt like the developers were rushing to catch the wave instead of building a proper tool We took our time, did months of RnD and finally came up with something better than what others in the market are doing. It’s got better design output. While it’s still in beta, I wanted to show HN what we did. Will appreciate the feedback when you guys try it out. Here is the link to signup for the beta:…
2024 · dorik.com
- 16AO
Hey HN, My workflow for any complex queries is to ask it in multiple AI chats (Gemini, Claude, o3,..) in parallel and then continue the conversation with the chat response that I found the most useful. I built a simple open source app that queries 10+ AI models at once and summarizes their answers with a selected combiner AI model. There's a GIF in the github repo that shows it in action. You can try it on your local machine: https://github.com/Nexarithm/multi_model_chat If you are interested, I also made a detailed blog post on technical details, feature of the personal…
2025 · github.com
- 17IC
For the last few months I have been analysing Peter Lynch’s books on stock picking and doing prompt engineering to check if AI could create useful stock analyses. To my surprise it started making reports that allow me to understand companies much faster with well cited sources. I hope you find it interesting and useful :) Perter Lynch’s books I analyzed: Learn to earn, One up on Wall Street, Beating the street
Jun 2026 · github.com
- 18AA
I'm a VP of Engineering with 20 years in the field. I've been thinking deeply on why AI is breaking every engineering practice, and it led me to the conclusion that the Agile Manifesto's values need updating. The core argument: AI made producing software cheap, but understanding it is still expensive. The Manifesto optimizes for the former. This addendum shifts the emphasis toward the latter. Four updated values, three refined principles, with reasoning for each. Happy to discuss and defend any of it.
Mar 2026 · github.com
- 19IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 20AN
Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
- 21IM
Hey HN! Thank you for all the support and feedback on my original submission 2 months ago. I've been improving the backend using a MCTS/AlphaZero approach and it's currently producing much better results. My long term goal is to allow users to manage multiple projects, deployed autonomously, both from scratch and by making continual updates all prompted with natural language. The cost of each project has been lowered to $9 as performance with smaller models has improved (I migrated from Claude-3-Opus to gemini-1.5-flash). Thanks for checking it out!
2024 · saas-quick.com
- 22IH
I started off with a simple goal: port the game Soldat to Typescript. I ended up not only with the game, but with a full system to train AI bots to play my own game, building a neural net to train new bot AIs from tens of thousands of simulated matches with different strategies. It also runs live all the time on my website and has a news dashboard and you can replay any game exactly. It all was a very fun way to dig into Fable's capabilities. The bill if I had done this with API billing? Nearly $2000.
Jun 2026 · soldat.bobbby.online
- 23S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 24WB
Hey HN: Kaveh here, founder of https://www.usage.ai/ We help companies drive down AWS, GCP, and Azure spend. Why? Because the way it's done now is a pain. DevOps and Software Engineers end up spending time managing costs rather than focusing on business problems. I have been building Usage AI for almost 4 years now (4 year anniversary in 1 month from now!) with an incredible group of founding people. We started as a product just to help lower AWS EC2 costs, and now we do all major AWS services (such as RDS, OpenSearch, ElastiCache, and Redshift with more on the way) and other…
2024
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