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repliedto fdaudens's post 25 days ago Ever wanted 45 min with one of AIās most fascinating minds? Was with @thomwolf at HumanX Vegas. Sharing my notes of his Q&A with the pressācompletely changed how I think about AIās future:
1ļøā£ The next wave of successful AI companies wonāt be defined by who has the best model but by who builds the most useful real-world solutions. "We all have engines in our cars, but thatās rarely the only reason we buy one. We expect it to work well, and thatās enough. LLMs will be the same."
2ļøā£ Big players are pivoting: "Closed-source companiesāOpenAI being the firstāhave largely shifted from LLM announcements to product announcements."
3ļøā£ Open source is changing everything: "DeepSeek was open source AIās ChatGPT moment. Basically, everyone outside the bubble realized you can get a model for freeāand itās just as good as the paid ones."
4ļøā£ Product innovation is being democratized: Take Manus, for exampleāthey built a product on top of Anthropicās models thatās "actually better than Anthropicās own product for now, in terms of agents." This proves that anyone can build great products with existing models.
Weāre entering a "multi-LLM world," where models are becoming commoditized, and all the tools to build are readily availableājust look at the flurry of daily new releases on Hugging Face.
Thom's comparison to the internet era is spot-on: "In the beginning you made a lot of money by making websites... but nowadays the huge internet companies are not the companies that built websites. Like Airbnb, Uber, Facebook, they just use the internet as a medium to make something for real life use cases."
Love to hear your thoughts on this shift! repliedto Kseniase's post 3 months ago 6 Comprehensive Resources on AI Coding
AI coding is moving fast, and itās getting harder to tell what actually works. Agents, workflows, context management and many other aspects are reshaping how software gets built.
Weāve collected a set of resources to help you understand how AI coding is evolving today and what building strategies work best:
1. https://huggingface.co/papers/2508.11126
Provides a clear taxonomy, compares agent architectures, and exposes practical gaps in tools, benchmarks, and reliability that AI coding agents now struggle with
2. https://huggingface.co/papers/2511.04427
This survey from Carnegie Mellon University shows causal evidence that LLM agent assistants deliver short-term productivity gains but have lasting quality costs that can slow development over time
3. https://huggingface.co/papers/2510.12399
Turns Vibe Coding from hype into a structured field, categorizing real development workflows. It shows which models, infrastructure, tool requirements, context, and collaboration setups affect real software development outcomes
4. https://huggingface.co/papers/2511.18538 (from Chinese institutes and companies like ByteDance and Alibaba)
Compares real code LLMs, shows how training and alignment choices affect code quality and security, and connects academic benchmarks to everyday software development
5. Build Your Own Coding Agent via a Step-by-Step Workshopā¶ https://github.com/ghuntley/how-to-build-a-coding-agent
A great guide that covers the basics of building an AI-powered coding assistant ā from a chatbot to a file reader/explorer/editor and code search
6. State of AI Coding: Context, Trust, and Subagentsā¶ https://www.turingpost.com/p/aisoftwarestack
Here is our in-depth analysis of where AI coding is heading and the new directions we see today ā like agent swarms and context management importance ā offering an emerging playbook beyond the IDE
If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe Organizations
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