Show HN: Backtest a futures strategy described in plain English https://ift.tt/3aflDG6
Show HN: Backtest a futures strategy described in plain English https://askfutures.com June 29, 2026 at 03:22AM
Show HN: Lorentzian CSS Duality in Causal Diamond Quantum Error-Correcting Codes https://ift.tt/Gn2RHLS
Show HN: Lorentzian CSS Duality in Causal Diamond Quantum Error-Correcting Codes https://ift.tt/8jzufJN June 29, 2026 at 02:33AM
Show HN: Sidequest is a better /btw for Pi https://ift.tt/GD3n4br
Show HN: Sidequest is a better /btw for Pi https://ift.tt/7z4io9M June 28, 2026 at 11:06PM
Show HN: Clanker TV https://ift.tt/auWR2X7
Show HN: Clanker TV https://botflix.tv/ June 28, 2026 at 04:30AM
Show HN: role-model, a router for hybrid local/cloud AI https://ift.tt/08yY4ph
Show HN: role-model, a router for hybrid local/cloud AI Hey everyone, I'm launching role-model today: a routing protocol, a reference router runtime, and an extension for Pi that allows for better informed routing decisions. role-model is mostly deterministic, with fallback to a controller model, that routes requests based on a chosen routing strategy. the protocol is structured around assigning domains and roles to models, where requests sent by consumer applications like Pi have task types to enrich routing metadata and thereby accuracy. you can to run the built-in benchmark to compare performance of models across speed, quality and cost, as well as observed performance on real tasks. I have a diagram on how routing works in [0]. The runtime supports local models, either directly to your local endpoint (LM Studio, llama.cpp etc), or routing between multiple local models via vendored llama-swap. Since there was another model router post yesterday where people discussed the basics of routing, I will focus on discussing some of the interesting learnings I've made building and testing this: 1. Model routing is essentially trying to predict the future: which model will perform optimally (based on criteria defined by the user) on this request? 2. After you have routed the request, you want to evaluate if it was the right decision or if some other model would have performed better 3. You also realize that having the router assess difficulty (among other things) to make decisions by itself is far from ideal - we'd prefer to have the consumer application work with the router to define what the request needs 4. You also realize that it becomes much easier, decisions become much accurate, and the outcomes of routing becomes more impactful when there is more of a distinction between models For point 2, I will be launching evals that you can run locally to benchmark models in your pool on the same requests. The outcomes here can then be used for point 1, as input when routing new requests. For point 3, I've built the pi-role-model package for Pi, which lets the Pi agent inject role_model.intent metadata including difficulty, preferred roles or even specfic model ids, required capabilities (say tool use or image input) and so on. You should be able to customized this further in Pi, and route in additional ways by changing metadata. This is why I've also built the role-model routing protocol. For point 4, what model routing really does as a second order effect is create a market for specialized models - models that may or may not be smaller, could be cheaper or more expensive, may be locally runnable. It makes little sense to route between two frontier models (GPT 5.5 and Opus 4.8); it makes more sense to route between models where one of the factors of quality, speed, cost is a multiple of the other candidate models, and it makes even more sense to have specialized domain models: code, prose, math and science, visuals and so on. It is at this stage model routing becomes really valuable. While role-model has a reference runtime that I'm continuously building out (there's lots to do to improve routing, as well as give users more granular control over routing decisions, and also ways to improve cross-model caching and also add techniques like FastContext), the ultimate goal of role-model is for there to be a standard protocol for inference requests that is used by consumer applications, so that the provider, be it a router middleware or an inference provider, will be able to route to a model that strikes the best balance between cost, speed and quality and also respects user choices, and even lets the user control these preferences to use local models for some tasks and allow cloud for others. Links: [0] role-model - the case for a model routing protocol: https://ift.tt/RKhbtXy... [1] GitHub: https://ift.tt/8YREbKM [2] Docs: https://role-model.dev/ https://ift.tt/8YREbKM June 28, 2026 at 12:46AM
Show HN: Hacker Times – HN Reader https://ift.tt/gcJ7xXf
Show HN: Hacker Times – HN Reader This has been a play project for a little while, trying to build an interface that's comfortable to read on any device. It's a bit of a mashup of a Pocket-like interface, digital newspaper and Hacker News. https://ift.tt/dz518K3 June 28, 2026 at 12:53AM
Show HN: Aerial-autonomy-stack – open-source perception-based drone swarms https://ift.tt/2mJKc9G
Show HN: Aerial-autonomy-stack – open-source perception-based drone swarms Combining (1) open-source autopilots with (2) open-source ROS2 simulation/navigation/mapping packages and (3) open-source, hardware-accelerated computer vision should not be hard, but it is. aerial-autonomy-stack aims to solve this by collapsing (1) multi-robot, (2) software-in-the-loop, (3) hardware-in-the-loop simulation, (4) CI/CD, and (5) deployment into a single containerized workflow. The end-to-end stack can be re-created/simulated with a one-liner (45') build on Ubuntu >=22. https://ift.tt/PvjSs8B June 27, 2026 at 03:07AM
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