Jan Čurn

Imagine enhancing your development workflow with just nine lines of code. Philip Zeyliger's latest article on Sketch.dev reveals how a simple loop can empower large language models to act as autonomous coding assistants. By integrating tools like bash, these LLMs can execute commands, adapt to various environments, and handle intricate programming tasks, all while reducing manual effort. This minimalist yet powerful approach signifies a paradigm shift in software development, where automation and AI collaboration become the norm. How are you leveraging AI to optimize your development processes? Read the full article here: sketch.dev #AIinDevelopment #Automation #LLMAgents

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I’m excited to share a new benchmark, MIPfeas, which shifts the focus in Mixed-Integer Programming from "final optimality" to "early feasibility." Using the Primal Integral metric, we evaluated how solvers like HiGHS, SCIP, and NVIDIA cuOpt deliver high-quality feasible solutions within a short time window. While direct comparison with commercial MIP codes remains difficult, we have grouped the results of three major commercial solvers—COPT, CPLEX, and Xpress—into three synthetic baselines: the Virtual Best, Mean, and Worst Commercial Solvers in this benchmark. A huge thank you to Hans Mittelmann, the NVIDIA team (Burcin Bozkaya, Akif Çördük, and Chris Maes), and ZIB Zuse Institute Berlin for the technical collaboration. Special thanks also to Timo Berthold, Gerald Gamrath, and Ferenc Katai for their expert insights. Check out the full results here: https://lnkd.in/dSfNc58v #Optimization #MIP #GAMS

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The software development landscape is undergoing a significant transformation as AI agents become integral to the process. Yoko Li's recent article, "Emerging Developer Patterns for the AI Era," delves into nine key patterns that illustrate this shift. From rethinking version control with AI-native Git to evolving dashboards into dynamic, conversational interfaces, these insights are crucial for professionals navigating the intersection of AI and software development. As we integrate AI more deeply into our workflows, how do we adapt our tools and mindsets to collaborate effectively with these new digital teammates? Read the full article here: https://lnkd.in/gxkjwdpM #AI #SoftwareDevelopment #Innovation

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I am proud to announce the official release of Sindhi NLTK on PyPI—the first Morphology-Aware, Hybrid-Neural NLP Toolkit designed to address these core challenges natively. Key Achievements & SOTA Metrics: Solving Subword Shattering: We introduced a novel "Linguistic Shield" pre-processing layer that protects the integrity of unique Sindhi alphabet clusters. SOTA Efficiency: Our logic reduced Token Fertility from a standard 4.15 (Llama-3) to an industry-leading 1.06 fertility rate on a 43,784 sample SFT dataset. Context Optimization: This reduction in token bloat effectively quadruples the context window efficiency for Sindhi LLMs, making training and deployment significantly more compact, accurate, and memory-optimized. Full Pipeline Support: Beyond tokenization, Sindhi NLTK offers contextual normalizers, a robust morphological stemmer, and context-aware sentiment analysis. My mission is to bridge the linguistic efficiency gap, moving beyond "translation-based" NLP toward natively intelligent models that respect the unique stylistics and affective registers of South Asian languages. Join us on this journey to preserve and advance computational linguistics for Sindhi. #NLP #Sindhi #Linguistics #MachineLearning #AakashMeghwar #LowResourceNLP #TechInnovation #AI #Preservation #OpenSource

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Today at Rumi Labs, Inc. we launched our Desktop App, running our proprietary SOTA edge VLM optimized for understanding complex multi-modal narratives. This makes Rumi the 1st-ever distributed Vision-Language Model network for Media in production. If you want to help build the Narrative Intelligence Layer for AI, download the Rumi extension at http://rumi.io

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Love this direction. Designing rewards that are dense while keeping benchmarks hard to hack is the right way to push LLM evaluation forward. I also appreciate how the paper introduces example-specific rubrics — making LLM judging aligned with actual user expectations instead of vague, artificial criteria. Clear, grounded rubrics are exactly what the evaluation ecosystem needs. Highly recommend reading. 👇

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AI in our lifetimes and at this moment is on the brink of becoming the most consequential thing in human history in comparison to all transformative predecessors (axes, fire, wheels, language, writing, guns, steam engines, factories, oil, electricity, cars, trains, planes, and computers) combined. In this TED Talk, Mustafa Suleyman, CEO of AI at Microsoft, offers an insightful answer to the question "What is an AI anyway?" Working his way to his take on an answer, he considers the consistently inevitable Pandora's Box of positive and negative consequences of all other global societal sweeping transformative discoveries and exhorts his audience on the importance of taking stock and caution for the consequential mix that will accompany AI's maturation. https://lnkd.in/eNQ2BGgX

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Raif now has support for LLM evals! If you're building Ruby on Rails apps with LLM's, you can now use Raif to evaluate the quality of LLM responses for different models. Allows you to iterate your LLM prompts without fear of completely breaking the responses. Also includes built-in support for using LLM-as-judge in your evals. https://lnkd.in/g3xcer-h

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Building a Production-Ready Website with Autonomous AI Swarms After a year of R&D on multi-agent systems, our website needed an upgrade. Instead of weeks of dev time, I tested our NOVAE approach: autonomous AI swarms. The process: Prep inference-ready data, configure swarms with roles and instructions, run tests, create requirements docs, and let it run overnight. Results after 10 swarm cycles: 561 files created, 75K lines of React/TypeScript code, 19 pages with interactive demos, SEO-optimized CMS with 15+ articles, and full CI/CD pipeline. Not perfect: UI issues needed manual fixes, one swarm crashed. An experiment with a vague prompt ("Create a showcase feature") yielded good ideas but rough execution. Key takeaway: AI swarms excel with clear data and requirements, dramatically accelerating development. But there's no magic bullet - human oversight matters for UX and quality. #AISwarms #AgenticAI #BuildInPublic

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Using LLMs to generate structured output is easy. But building high-quality document extraction with precise citations and reasoning for every key in the extracted output is harder. LlamaExtract is our agentic document extraction service, over even the most complex documents and input schemas. By toggling 1-2 options, you can get the following on each extracted key/value pair:  1️⃣ Precise reasoning on where that data came from 2️⃣ A list of page citations and matching text on where the K/V pair came from - it can be derived from a combination of underlying context! Call us as an API - and easily plug it into any downstream agent workflow. To get started, sign up here: https://lnkd.in/gDY88Dtn We have an entire directory of examples to help you get started: https://lnkd.in/gsvKqrNV

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A new study from Carnegie Mellon, “Does AI-Assisted Coding Deliver? A Difference-in-Differences Study of Cursor’s Impact on Software Projects” that looks at a more recent period, tracking code quality in projects using GenAI tools up to mid-2025, finds that AI briefly accelerates code generation, but the underlying code quality trends continue to move in the wrong direction. https://lnkd.in/gm6dxb8W

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