Results for “agents”

…introduces AgentEval, a project for visualizing why autonomous agents fail, and proposes tracking recurring failures and incorrect outputs in Code Interpreter. His example is a hallucinated dependency during ffmpeg-related work, although he…

…context did not remove the review bottleneck. Experimental agents produced large amounts of code that were difficult to verify; debugging could take longer than writing it manually. A planned chat workflow borrowed from Code Interpreter:…

…Alessio sees an opening for founders in what was absent: agents working in the background. OpenAI’s GPTs still center on synchronous, one-on-one chat, while lower model prices could benefit asynchronous applications. Swyx similarly reads…

…These configured assistants are treated as an early step toward agents, with an important limit: agent robustness depends on the underlying model. GPT-4 Turbo appears promising because of better function calling and tool switching, but what…

…and sent many responses as drafted. The imagined next step was agents coordinating schedules or contract details and returning proposed actions. That requires faithfully representing the user’s interests; reliable handling of hallucinations…

…work. The team envisioned a gradual path from notebooks to agents: 1. A human chooses and adds action steps. 2. A model predicts which action the human would choose. 3. Predictions reliable enough for the task become automatic actions. This…

…OpenAI representative encourages testing previously difficult agents and applications, acknowledging that broader access and higher rate limits still required work. Lindsey explains that the o1 naming reset signals a shift toward reasoning.…

…layer to try Anthropic. Asked why Bloop has not moved toward agents, Knight-Webb challenges the premise. Its pipeline already consists of GPT in a while loop with function calling, which he says clearly counted as an agent nine months…

…while internet video lacks the controllability needed to train agents that act in an environment. Simulation offers a middle ground, but building it requires curating assets, composing situations, and providing varied states for…

…own data, build a query index, connect processing steps and try agents. Exploring those tools is how developers learn what models can do and how to assemble them into useful workflows.

…not work on inference-time scaling. Pokémon shifts the focus to agents that must act over long sequences. The guest says the Gemini work under discussion included no Pokémon-specific work. They value the game as an occasional capability…

…and AlphaZero have game simulators; broadly capable embodied agents lack an equivalent general environment. He defines world models by sequential action control. A consistent clip may contain world knowledge, but caption-level control alone…