562 karma · joined December 8, 2022
The idea sounds cool though
a surprisingly large fraction of production workloads can be handled by smaller models with the right scaffolding. it's often easier to switch to a larger model than to engineer those pieces, so many teams never bother.
my intuition is that a lot of the current "ai cost crisis" is really an orchestration problem rather than a model pricing problem. before asking whether frontier pricing is sustainable, i'd first ask how much of that spend is simple tasks being sent to the smartest available model by default.
my bet for the next few years is that the model itself stops being where the value is. frontier models will become more like commodities, and the real difference will be the layer around them as routing each task to the cheapest model that can do it well, verifying the output, and only escalating when needed.
eventually, asking "which model do you use?" will sound a bit like asking "which cpu do you use?" the engine still matters, but the system built around it matters a lot more.
https://github.com/dashersw/erste https://github.com/dashersw/regie
https://github.com/dashersw/erste https://github.com/dashersw/regie
Our avatar was a really hard one to handle, but I think it's still really good. All we did was connect the APIs. LemonSlice is really cool.
Started when I was struggling to read books in English. Pushed an open source version back then (https://github.com/baturyilmaz/wordpecker-app), later added more features, and now working on a mobile app.
Recently started testing alpha version, fixing bugs and introducing new features right now (https://alpha.wordpeckerapp.com/).
My end goal is to build: an AI language learning companion that knows what you read, listen, and watch, knows you as a friend (real life, who you are), then helps you improve using that context. If you're B1 at language, it creates a personalized path to get you to B2, then C1, and so forth using your context.
The idea is simple, but I think it could be really cool: an autonomous agent that actually manages an entire radio station. It creates its own shows, play copyright-free tracks, shares the daily program schedule on social media and the website, and later I want to add guest appearances too and live 7/24.
How does apps for Paper Pro works?
"AI has a product problem. Not a model problem.
Models are making capability leaps every few weeks but AI-native product innovation hasn’t kept up.
Most products are forcing AI into existing UX patterns rather than rethinking an AI-native experience from first principles. Parallels to early mobile (2007-2010) - for years, mobile meant shrinking your website into your phone until Uber reimagined transportation.
To be clear, there are lots of amazing AI-first products. See NotebookLM, Lovable, Stitch, Flow. But the pace of innovation on models has been faster, for a lot of reasons which I will explore in future posts."
A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally.
I've been playing around with agents, MCP servers and embedded systems for a while. I was trying to figure out the best way to connect my real-time devices to agents and use them in multi-agent workflows.
At OpenServ, we have an API to interact with agents, so at first I thought I'd just run a specialized web server to talk to the platform. But that had its own problems—mainly memory issues and needing to customize it for each device.
Then we thought, why not just run a regular web server and use it as an agent? The idea is simple, and the implementation is even simpler thanks to MCP. I define my server’s endpoints as tools in the MCP server, and agents (MCP clients) can call them directly.
Even though the initial idea was to work with embedded systems, this can work for any backend.
Just wanted to share and would love to hear your thoughts—especially around connecting agents to real-time devices to collect sensor data or control them in mutlti-agent workflows.
Repository: https://github.com/openserv-labs/mcp-proxy
I’m starting to feel like most agentic frameworks are more theoretical than practical—except for easy-to-implement text embedding capabilities like those in LangChain and AutoGen. That said, I think platforms like OpenServ, which provides SDK while allowing devs to leverage other agents from the same ecosystem, will be the ones to win. I’m also planning to explore other platforms like LangFlow, CrewAI, n8n, etc., to compare different approaches.
OpenServ's demo: https://www.youtube.com/watch?v=l9Vpko9GXRg
- LangTurbo (by @sebnun) - langturbo.com : Learn through podcasts with transcriptions and contextual word definitions
- Nuenki (by @Alex-Programs) - nuenki.app : Browser extension that translates appropriate-difficulty sentences across websites, with hover-for-definitions feature
- Manabi Reader (by @wahnfrieden) - reader.manabi.io : Japanese-focused integrated reader with SRS and Anki integration
- (by @muth02446) - Spanish: appicenter.net/Apps/VocabES/ - English: appicenter.net/Apps/VocabEN/ : Uses spaced repetition and audio for basic vocabulary learning
- Vocabuo (by @kebsup) - vocabuo.com : Combines SRS flashcards with ebook/YouTube/website reader, using AI for content generation
- LingoStories (by @laurentlb) - github.com/laurentlb/lingostories/ : Open-source language learning tool
- Turkish Learning Tool (by @learning-tr) : Browser extension for colloquial translations with audio and pronunciation features
- Language Reactor (by @davidzweig) : Planning to open-source soon, looking for contributors
Note: above list is summarized by Claude 3.5 Sonnet.
Not: Bu benim en sevdiğim şarkılardan biridir!! (it's one my fav songs)
*thanks again!!