255 stories
DeepSeek V4 is a family of models, and only some of them accept images. This guide maps every V4 slug on our catalog to its input modalities, shows how to send an image to the two that read images, and shows how to put a vision model in front of the text-only ones.
Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.
Moving from closed to open source models can take weeks, not years. A five-stage playbook: discover, evaluate, adapt, decide, and production.
Ollama only runs the model, so the answer depends on which one you pull and your memory. What local models do well, where Claude wins, and a verdict.
Most large-scale AI training runs operate at 35–45% Model FLOPS Utilization (MFU). You're paying for twice the compute you're using.
An LLM judge is a second model that scores an agent's output against criteria you write in plain language. This guide explains where a judge fits beside deterministic tests and human review, then shows how to add one to an Ori Eval test, compare candidate models, calibrate the threshold against…
Listen and speak at once with Full Duplex in Famulor. Compare Pipeline, Half Cascade and Realtime, with model benchmarks, TTS data and use cases.
Staff AI Engineer - Agent Architecture & Behavior — San Francisco, CA · full-time · On-site · $250k-$325k
GPT-6 Astra earns a clear verdict: if your work involves multi-step digital tasks, this is the most meaningful upgrade OpenAI […] The post GPT-6 Astra Review: Is It Worth the Upgrade in 2026? appeared first on AiZolo.
Today's haul: 18 tools · 9 resources · 31 reads · 12 numbers · 22 things that happened. Every tool, resource and read below has a working link. No link, no listing.
Consider what an AI agent actually has to do when asked to research a sector and propose next month's product priorities.It needs to spot emerging trends, read primary sources, compare user feedback across platforms, validate key metrics, and turn its findings into a report, a dataset, and
AI agents have shown great potential in automating real-world tasks, such as conducting a Google Search, reading local computer files, or executing generated Python scripts. To achieve such agentic workflows, LLMs need to learn how to use tools correctly and efficiently.
Why AI voice agents forget rules after 5 turns and how to fix instruction following with deterministic conversation logic and automated compliance monitoring.
I run this episode solo. I explain local AI in plain terms: the model runs on hardware I control, and a cloud model runs somewhere else. I map the four pieces of the local AI landscape — the model, the warehouse, the software, and the workflow — and I define the words that beginners meet first:…
Notebook by Stefano Fiorucci In this experiment, we will use Large Language Models to perform Information Extraction from textual data. 🎯 Goal: create an application that, given a URL and a specific structure provided by the user, extracts information from the source.
Notebook by Stefano Fiorucci In this experiment, we will use Large Language Models to perform Information Extraction from textual data. 🎯 Goal: create an application that, given a text (or URL) and a specific structure provided by the user, extracts information from the source.
An AI agent is an LLM in a tool-calling loop. How loops work, why they stall or run away, and the 3 architecture decisions that matter in production.
Every tool an AI agent can use has to be described to it first — and that description is re-read at the start of every conversation. Published measurements put a five-service setup at roughly 55,000 tokens before a single request is read.
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