Diese Sammlung filtern + ThemenAlle Themen inference performance infrastructure optimization model deployment workflow integration rationale AI model category adoption model design and deployment seed fund sizing acquisition strategy venture fund strategy adapter layer design MCP connector approval connector architecture security review process MoE scaling efficiency numerical precision optimization training infrastructure performance content strategy public-private ecosystem design data center development realism infrastructure credibility stakeholder inclusion local acceptance data center development process product launch and pricing AI differentiation strategy market problem framing privacy and hardware design AI industry and gaming industry misalignment AI-native game design AI product design principle game revenue measurement AI misuse in game development fine-tuning efficacy AI alignment and principal-agent problems AI agent coordination design Agent delegation in coding tools Human vs. agent organizational constraints AI planning capabilities Multi-agent coordination web standards development test observability test automation design AI in software development standards development trust boundary design agent security risk performance-security tradeoff AI infrastructure security AI alignment and containment limits Empirical assessment of containment failure technical origin story distributed file systems Self-directed AI adoption AI-assisted software development AI compute allocation AI device strategy agent development usability agent adaptability pricing model AI marketplace integration customer case studies customer discovery case studies AI model capability comparison AI model performance and cost AI model reliability and token limits Application building iterations Coding task quality Assistant evaluation efficiency Customer support workflows coding agent reliability goal-directed AI product-market fit cloud storage pricing history S3 pricing history S3 current pricing KI-Produkte KI-Sicherheit network effects of AI collaboration platforms AI agent collaboration challenges shared context in AI-augmented collaboration open science infrastructure developer tooling prediction uncertainty sales team development equity ownership in bootstrapped vs. funded startups founder sales capability reasoning modality post-transformer architecture motivation latent reasoning benefits KI-Infrastruktur Enterprise-KI communication design for AI AI team integration tool architecture limitation API design preference HTTP caching infrastructure CDN caching limitations AI-powered creator tools Algorithmic curation philosophy Content quality over AI origin AI model release multi-character TTS voice customization design thinking product complexity AI tooling KI-Forschung model deployment platform fine-tuning cost efficiency training duration AI infrastructure constraints qualification program design qualification scope limitation AI infrastructure justification AI developer education growth AI developer training scale Human advantages with AI interdisciplinary literacy Expertise and AI output quality Chip architecture Hardware flexibility AI infrastructure strategy KI-Suchsichtbarkeit Unternehmen aufbauen KI und die Wirtschaft AI intellectual property protection government data integration AI-assisted knowledge work evaluation latency tolerance in voice interactions session limits in voice agents voice agent function calling function selection accuracy media virality and AI narratives organizational safety practices AI safety discourse dynamics strategic AI adoption framework enterprise software sprawl problem identification gap AI-enabled tool creation open source sustainability open source licensing strategy license key distribution strategy AI evaluation criteria context layer efficacy enterprise AI architecture model distillation retrieval methodology static embedding performance KI-Suche real-time voice AI latency budget perceived latency thresholds production latency SLOs release governance for AI agents incremental deployment automated agent validation personal agent security agent autonomy agent architecture Legal implications of attribution failure Causal attribution in AI outputs Exact deletion method for influence testing AI/SaaS product governance AI development paradigm AI implementation barrier iterative AI interaction prompt engineering for beginners AI tool onboarding runtime governance control AI governance assumptions AI supply chain attack vector AI governance sovereignty AI agent privilege escalation managed AI services trade-offs AI risk mitigation Entscheidungsfindung ontology design for AI agents agent validation workflow ontology-based business logic enforcement neurosymbolic AI architecture AI system security and observability workflow automation KernelBench GPU kernel evaluation agent system design failure analysis in AI systems Human–AI performance tradeoffs in RE-Bench MLE-bench offline Kaggle benchmark AI deployment architecture RE-Bench AI agent evaluation methodology AI evaluation and generalization model evaluation open science and AI impact scaling AI creativity and scientific discovery founder and creator agency Open-Source-Software AI video workflow paradigm AI video-audio architecture AI video input flexibility AI agent evolution agent memory architecture enterprise adoption metrics runtime security design customer experience design product scope customer-centric metrics Focused teams Customer focus Leadership and feedback agent reliability threshold human-agent interaction developer experience AI adoption AI deployment strategy AI security AI governance AI personalization AI memory AI limitations in education AI research direction AI capability assessment AI research challenges PersonenAlle Personen Dion Harris Tim Fernholz Jason Lemkin Chenghao Wang Kyle Wiggers Jennifer Ferro Josh Dzieza Nilay Patel Aisha Malik Piero Molino Ethan Mollick David Burns Olivia Johnston Matthew Green Peter Mattis 王吉平 王博 Eric Eyken-Sluyters Ruben Gamez Rob Walling Simon Willison Gabriel Grinberg Daniel Vogel Curtis Allen Abhinay Kathuria Daksh Gupta Kevin Hou Zach Lloyd Mike McCormick Anastasis Germanidis John Coogan Jordi Hays Ivan Zhou Anthony Costa Ross Andrew Paquette Zuzanna Stamirowska Nick Kuhn Santi Subotovsky Neal Mohan Maggie Appleton Mckenzie Lock Eric Nguyen Hassan El Mghari Vishal Ganeriwala Fatima Tambajang Wade Foster Matt Pocock Richard Ho Liam Dunne Ali Ghodsi Ben Moore Anton Leicht Aaron Levie Ben Gilbert Evangelos Simoudis Jose Nicholas Francisco John Schulman Nathan Lambert Demetrios Brinkmann Tibo Sottiaux Han Wang Benedict Evans Adam Stacoviak Katie Parrott Stephanie Baladi Ajeya Cotra Ben Rudolph Logan Markewich Parag Agrawal Sachi Shah Maarten Grootendorst James Grimmelmann Zheng Dai David Gifford Pedro Moura Kayla Cinnamon David Rosenthal Daniel Whitenack Chris Benson Kareem Amin Frank Coyle Yash Poojary Chris Pedregal Tarek Mansour Lilian Weng Zubin Pratap Demis Hassabis Jean-Baptiste Kempf shridharathi Jensen Huang João (Joe) Moura Jim Goetz Mark McLaughlin Nikesh Arora Markus Villig Roelof Botha Maya Hoffree Ilkka Paananen David Risher Abhishek Fatehpuria Dan Gallagher Dex Horthy Jim Swanson Dario Amodei Chris Olah Amanda Askell Luis von Ahn Pieter Levels QuellenAlle Quellen How NVIDIA GPUs Help Accelerate OpenAI’s GPT-6 Astra Ultrafast Amazon releases its own Jev clone as decision models flood the web | TechCrunch 20VC x SaaStr: Anthropic’s S-1 Leaks, Instinct Hits $10 Billion in 33 Days, AMD Buys World Labs, and Meta Poaches MongoDB’s CEO How We Built Harvey’s Connector Library Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs Don’t be boring How Utah locals fought Kevin O’Leary’s AI data center Hearing tech startup Legato launches its AI hearing glasses | TechCrunch Piero Molino on AI in Games The Dot and the Swarm The State of Browser Testing - Software Engineering Daily Sidecars: A low-latency trust boundary for Sandboxes | Modal Blog Is sandboxing sufficient to contain rogue agents? Distributed databases with Peter Mattis When Glasses, Earbuds and Phones All Have AI, Who Will Unify Your “Second Brain”? | Snapdragon Summit 2026 Sierra joins the OpenAI Marketplace Episode 852 | Getting Big Name Customer Case Studies and More Listener Questions (with Ruben Gamez) Claude Sonnet 5.5 Customer testimonials published by Anthropic for Claude Sonnet 5.5 2026 in LLMs (so far) Comment: S3 Is the Future, S3 Is the Past KI-generierter Code konkurriert bereits mit menschlichem Code – Daksh Gupta, Greptile Dem Modell aus dem Weg gehen — Kevin Hou, Google DeepMind Softwareentwicklung wird zur Fabriktechnik – Zach Lloyd, Warp Von null auf eins in der KI-Sicherheit: Mike McCormick von Halcyon über die Gründung von 30 neuen Organisationen und den Gründer-Engpass Runways WorldPrompt und das Engineering von Echtzeit-Welten TBPN: John Coogan über persönliche Agenten und Werbung Our Investment in Ando: Modern Messaging for Teams and Agents How Open Science Can Help Researchers Prepare for the Next Pandemic Bootstrapping From a $500K Goal to a $50M Company What Comes After Transformers? Von AGENTS.md bis zum Einsatz im Unternehmen mit Nick Kuhn Rethinking Communication for Human and AI Teams | Emergence Capital Comment: We just shipped support for the ugliest part of HTTP: Vary YouTube CEO Neal Mohan: Why We’re Betting On AI And Not Afraid Of It Tool: Gemini 3.8 TTS Playground Design Engineering mit Maggie Appleton Parsing “Can” from “Should”: How Netflix's Product Team Got Thousands of Creatives to Trust an AI Overhaul 🔬 Biosicherheit ist ein KI-Wettrüsten – Eric Nguyen (CEO, Radical Numerics) How to train your own Jev for $17 TBPN: John Coogan über Agenten, App-Schnittstellen und Werbung NVIDIA Launches DSX Ready to Qualify Power and Cooling Products for AI Factories From Enablement to Execution, Egypt’s AI Ecosystem Reaches Production Scale The Overhang No Code ist Code: Zapier-CEO Wade Foster über Headless Tools, Zapier MCP & Automation Bench KI-Fähigkeiten mit Matt Pocock Inside OpenAI’s First Chip Wie man in der KI-Suche entdeckt wird Ali Ghodsi über das Lernen als CEO und die Bewertung von Managementpraktiken Anton Leicht über KI-Einsatz, wirtschaftliche Umbrüche und Risiken Aaron Levie von Box: Über die Neuerfindung im KI-Zeitalter und die Verbreitung in Unternehmen The Home Depot When Everyone Is 10x More Productive, No One Is Voice Agent Function Calling: Real-Time Data Lookup Guide KI-Forscher debattieren, wie nah wir der rekursiven Selbstverbesserung sind One resignation turned the embers of AI fear into a wildfire Computer-Use-Agents und die Zukunft des agentischen Internets Aufbau von Codex mit Tibo Sottiaux Simpler, outcome-based AI pricing AI, tools and transformation — Benedict Evans Forking Cal.com to closed source (Changelog Interviews #685) Katie Parrott über die Nutzung von ChatGPT zum Schreiben und zur Karrierereflexion From enterprise search to enterprise context: what AI agents actually need Ajeya Cotra über Agenten-Persistenz, Koordination und Benchmark-Betrug Städte großartig machen: Peregrines Nick Noone & Ben Rudolph Exploring Static Embedding Retrieval Parag Agrawal von Parallel: Ein neues Web für KI-Agenten aufbauen Real-Time Voice AI Stack for Agents: Architecture Guide Release governance: guardrails for agents at scale An Agent Is Just an LLM in a For-Loop When AI art has no author: MIT study finds generated images often can't be traced to any training data | MIT CSAIL The People Who Understand Don't Build. The People Who Build Don't Understand. | CrewAI GitHub Copilot app for Beginners: Write your first prompt Disney: Die Renaissance und das Imperium Reconstructing how OpenAI agents attacked Hugging Face Der Philosophen-CEO | Clay-Gründer Kareem Amin Why Agentic Systems Need Ontologies — Frank Coyle, UC Berkeley Yash Poojary über die Automatisierung von Wachstumsexperimenten bei Every Chris Pedregal über Startups und Arbeit jenseits von Meeting-Notizen Tarek Mansour von Kalshi gegen die Bundesregierung Harness Engineering for Self-Improvement Cartesia | A Guide to Choosing Voice AI Models The Walt Disney Company Demis Hassabis on Scientific Discovery and Personal Agency Jean-Baptiste Kempf über Open-Source-Codequalität, Wartung und sichere Builds How to make remarkable videos with Seedance 2.0 – Replicate blog Jensen Huang über Sicherheit von KI-Agenten, Führung und Vertrauen Orchestrating Self-Evolving Agents with CrewAI and NVIDIA NemoClaw | CrewAI Palo Alto Networks mit Nir Zuk & Nikesh Arora – Der Groll, der die Cybersicherheit veränderte Bolt mit Markus Villig – vom Bootstrapping in Estland zum weltweit führenden Mobilitätsanbieter Supercell mit Ilkka Paananen – Wie ein früher Pivot zu ‚Clash of Clans‘ und ‚Brawl Stars‘ führte Lyft CEO David Risher on paying drivers more and the shift to robotaxis Trade Winds: The Rise, Reckoning and Reimagining of Vlad Tenev | Index Ventures 12-Factor Agents: Patterns of reliable LLM applications — Dex Horthy, HumanLayer Gen AI Present and Future: A Conversation with Jim Swanson, CIO at Johnson & Johnson | Greylock Dario Amodei, Amanda Askell und Chris Olah über KI-Entwicklung und Sicherheit Duolingo CEO Luis von Ahn thinks AI has a lot to teach us Pieter Levels über das Bootstraping von Startups, KI-Bildmodelle und Indie-Entwicklung Runway Research | Introducing General World Models ErscheinungsjahrAlle Daten 2026 2025 2024 2023 Diese Sammlung filtern 1 Personen · 1 Quellen · 1 geäußerte Meinungen
Filter zurücksetzen IM KONTEXT
workflow automation Einen Standpunkt auswählen und das Originalgespräch nachverfolgen.
Lilian Weng 4. Juli 2026
Goal-oriented loops let models test and iterate on their own Quellenevidenz lesen ↗ IM KONTEXT workflow automation 1 ausgedrückte Meinungen
01 Goal-oriented loops let models test and iterate on their own Lilian Weng 2026-07-04 Gleich große Sektoren dienen der Orientierung, nicht der Rangfolge.
01 Goal-oriented loops let models test and iterate on their own Lilian Weng · 2026-07-04 Standpunkte 1–1 von 1 · Neueste Quellen zuerst
Zurück 1 / 1 Weiter
Ausgewählter Standpunkt
Goal-oriented loops let models test and iterate on their own Defining a workflow where the model can operate, test, and iterate is central to automation. A common pattern is a goal-oriented loop: plan → execute → observe/test → improve → execute again until the goal is achieved. The loop may proactively ask users for clarification on task specification or execution preferences.
Dies sind individuelle Standpunkte, keine Messung einer Übereinstimmung. Das Quellenmaterial bleibt in der Originalsprache.
Stützende Belege
Lilian Weng · 4. Juli 2026
Harness Engineering for Self-Improvement Originalauszug
Defining a workflow in which the model can operate, test, and iterate is a key design for automation. Karpathy’s autoresearch repo ( https://github.com/karpathy/autoresearch ) is a clean example of how such a workflow can be constructed. A common workflow follows a goal-oriented loop of plan, execute, observe/test, improve, and execute again until the goal is achieved. The process may trigger proactive requests to users for clarity in task specification or execution preference. Quelle & Kontext
Die Daten beziehen sich auf Quellen, nicht auf Änderungen der Überzeugung. Texte ohne geprüfte Übersetzung bleiben in ihrer Originalsprache.