Nedim Renesalis @SyProtSid
Computer Research Scientist github.com/NedimRenesalis Vienna, Austria Joined October 2021-
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Network effects in LLM wrapper apps are irrelevant. Placing ads a la Google's adwords inside LLM outputs will have to significantly change the way they are displayed in order to stay. A lot of innovation inbetween to come. @thinkymachines
Why dont you have a comprehensive AI VC funding list which is updated daily? It would be helpful. @TechCrunch
Many confuse advanced pattern matching with AI. The grey zone in between is fueled partly by good marketing on the builders' side and partly by low level of computer science knowledge on the consumers' side. Eitherway, this is the sweetspot where money is currently made.…
If an AI bubble means that every weird idea + AI = great funding rounds we are a bubble, then yes, we are in a bubble, but actually few great painpoints of efficiency in the real economy, society, medicine, tech, law and science have been solved by AI yet. There is still a long…
AI agents vs. walled gardens: Agents wont be able to effciently use their capabilities without having access to APIs. Example: how should an agent efficiently book a trip if it does not have access to the API of a given travel booking site like oyster.com ?…
a2a systems will be measured by coordinability more then single agent performance. @speedrun
The LLMs today are like cloud computing services in their early days. Few winners will emerge. You will use their APIs to power your apps. What will you build? #ArtificialIntelligence
1. The Death of Copilots: Why the Future of AI Coding Isn’t What You Think By Nedim Renesalis / August 2025 AI copilots are already yesterday’s news. While developers are still getting comfortable with GitHub Copilot and similar tools, a fundamental transformation is quietly…
2. The 70% Problem: Why Copilots Are Fundamentally Limited If you’ve used AI coding tools extensively, you’ve probably experienced what experts are calling the “70% problem.” You describe what you want, the AI generates impressive-looking code, and you think you’re almost done.…
3. As Addy Osmani, Google’s Head of Chrome Developer Experience, puts it: “While engineers report being dramatically more productive with AI, the actual software we use daily doesn’t seem like it’s getting noticeably better.” The productivity gains are real, but they’re not…
4. The implication is profound: as AI tools become more sophisticated, deep technical expertise becomes more valuable, not less. The Corporate Revolution: Agentic AI Is Already Here While the tech community debates the limitations of copilots, corporations are quietly adopting…
5. What’s agentic AI? Unlike copilots that wait for your commands, agentic systems can plan, execute, and iterate on complex tasks autonomously. They don’t just generate code—they understand requirements, test their solutions, fix bugs, and even deploy applications. Companies…
6. Take Amazon Q Developer, which has evolved beyond simple code generation to include five specialized agents: one for code generation, another for Java version upgrades, one for documentation, another for security reviews, and one for test generation. Each agent has deep…
7. The English-First Revolution Perhaps the most revolutionary aspect of emerging AI development tools is what experts call “English-first” development environments. Instead of learning programming languages to communicate with computers, AI systems learn to understand human…
8. Early implementations are already demonstrating remarkable capabilities. Developers can turn Figma designs into working web applications in minutes, describe complex business logic and receive complete implementations, and collaborate with AI systems on architectural decisions…

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