|
Evaluating LLMs & AI Agents: RAG, LLM-as-a-Judge & Production Evaluation Engineering
DataSpeaks
Videoen gennemgår, hvordan LLM'er og AI-agenter kan evalueres på en måde, der går langt ud over traditionel maskinlærings-nøjagtighed. Den præsenterer et flerdimensionelt scorecard med fem centrale dimensioner: korrekthed, jordforbindelse (groundedness), relevans, fuldstændighed og sikkerhed/politik, og illustrerer med et konkret eksempel, hvordan et sprogligt flydende svar kan fejle på alle dimensioner. Videoen dækker desuden metrikker som præcision, genkaldelse og F1-score i generativ AI-kontekst, samt emner som LLM-som-dommer, biasreduktion, gyldne datasæt og kontinuerlig produktionsovervågning. Målet er at give seerne redskaber til ikke blot at bygge kraftfulde AI-systemer, men også at bevise, at de er pålidelige og sikre.
|
|
MSCE: Memory-Skill Co-Evolution for Long-Horizon LLM Agents
AI Papers Explained
MSCE (Memory-Skill Co-Evolution) is a framework designed to improve the performance of large language model (LLM) agents on long-horizon tasks by enabling the simultaneous evolution of both memory and skill components. The approach allows agents to accumulate and refine experiential knowledge over time, leveraging past interactions to enhance future decision-making and task execution. By co-evolving memory and skills together, the framework aims to address limitations of static LLM agents in complex, multi-step environments.
summary from description (full transcript skipped)
|
|
The rise of agentic AI in smartphones
BNN Bloomberg
Agentic AI is increasingly being integrated into smartphones, marking a significant shift in how artificial intelligence functions on mobile devices. Unlike traditional AI assistants, agentic AI can autonomously perform complex, multi-step tasks on behalf of users. This development represents a growing trend among smartphone manufacturers and tech companies competing to embed more capable, action-oriented AI into their devices.
summary from description (full transcript skipped)
|
|
I built my first local AI agent
PCWorld
The presenter builds a local AI agent system using an MSI Cubi NUC mini PC as the agent host, a Framework Desktop (Strix Halo, 64GB RAM) running LM Studio with a Qwen 3 A35B model, and connects to it via Telegram on his phone. With guidance from Wendell of Level1Techs, he chose Hermes Agent over Open Clou for its more mature architecture, and configured it to run entirely locally for privacy and cost reasons. His practical goal was to have the agent scrape the web and deliver daily news digests for his two podcasts covering PC hardware and handheld gaming. As of the video's end, the system is functional but the digest feature is inconsistent, with web scraping via Tavily not working reliably, leaving the project as a work in progress.
|
|
Continual Learning: How AI Agents Get Better With Every Use | Arjun Karanam, Trajectory
Sequoia Capital
Arjun Karanam fra Trajectory præsenterer virksomhedens platform for kontinuerlig læring af AI-agenter. Han argumenterer for, at nutidens AI-modeller ganske vist bliver klogere (højere IQ), men mangler erfaring – de starter altid som om det er "første dag på jobbet" – og at de millioner af tokens, agenter genererer under brug, i dag blot smides væk i stedet for at blive brugt til læring. Trajectory's løsning består i at indfange disse interaktioner, omdanne dem til belønningssignaler og specifikationer, og bruge dem til løbende at forbedre både modeller og værktøjer via teknikker som reinforcement learning (f.eks. SDPO). Målet er agenter, der bliver hurtigere, billigere og bedre jo mere de bruges.
|
|
Building autonomous multi‑agent workflows | Copilot Studio Updates August 2026
Microsoft Power Platform
Videoen fremhæver, hvordan Copilot Studio bruges til at bygge autonome multi-agent-arbejdsgange, med Graebel – en af verdens største udbydere af virksomhedsflytninger – som eksempel. Graebel håndterer nu over 50.000 serviceordreforespørgsler om året fuldt automatiseret via platformen, processer der tidligere var helt manuelle. Nøglen er arbejdsgange drevet af triggere, der lytter efter hændelser i underliggende systemer og bruger AI til at klassificere og rute opgaver til specialiserede downstream-agenter. Platformen giver desuden mulighed for test, iteration og løbende overvågning af arbejdsgangenes performance, så virksomheder kan optimere og skalere deres automatisering løbende.
|
|
Lithosphere Review: LITHO Token and AI Agent Blockchain Infrastructure
Zarx Crypto
Lithosphere is an AI-native blockchain infrastructure project designed for autonomous agents and Web4 systems, positioning itself as the foundational layer for a future where AI agents—not just humans—are active on-chain participants. Its infrastructure stack includes Lithic (an AI-native smart contract language), PPAL (a programmable identity layer), MultX (cross-chain coordination), NNS (decentralized naming and discovery), and the LEP-100 standards framework, with LITHO token serving as the utility layer across all components. The project had a pre-TGE milestone scheduled for July 28th and a final Token Generation Event set for September 15th, though these are distinct events and should not be treated as confirmed exchange listings. The video emphasizes that while the vision is ambitious, the project still needs to demonstrate developer adoption and real demand, and viewers are urged to do their own research and consult official channels before participating.
|
|
X-62 - The AI Fighter Pilot Just Got a Lot More Dangerous!
The Mover and Gonky Show
The X-62 VISTA is an AI-controlled experimental fighter jet developed by the US Air Force that has been advancing its autonomous aerial combat capabilities. Recent developments suggest the aircraft has demonstrated increasingly sophisticated dogfighting skills, potentially surpassing human pilots in certain combat scenarios.
summary from description (full transcript skipped)
|
|
Fetch.ai on Quasa.io — Full Review of Autonomous Agent Platform
QUASA
This video is a promotional explainer reviewing Fetch.ai, a decentralized AI platform that aims to build an "agentic web" where autonomous AI agents discover, collaborate, and transact with each other to complete complex real-world tasks — such as planning an entire family trip by coordinating specialized agents for flights, hotels, and restaurants simultaneously. The video outlines Fetch.ai's tech stack, including its ASI-1 language models, the Agentverse marketplace (hosting 2.7 million agents), and the UAgents open-source framework. A significant portion of the video promotes Quasa.io, a rewards platform with over 120,000 members, where users can earn QUA cryptocurrency tokens by testing AI and Web3 tools — including 1 QUA specifically for trying out Fetch.ai. The overall message is a combined pitch for both Fetch.ai's autonomous agent ecosystem and Quasa's crypto-reward system for early adopters.
|
|
My top secrets to running an AI Agent Workforce
Greg Isenberg
Alli K. Miller, en erfaren AI-ekspert med baggrund fra IBM og AWS, gæster Greg Isenbergs podcast for at diskutere, hvordan man opbygger og tænker om en AI-agentarbejdsstyrke. Hendes centrale pointe er, at "at administrere agenter" er et forældet begreb – i stedet bør man tænke som en SVP-leder, der sætter infrastrukturen og lader agenterne selv finde ud af, hvordan opgaverne bedst udføres. Miller beskriver sin egen opsætning med 34 AI-agenter ledet af en AI-stabschef kaldet Simon, og afslører at hendes bedste prompt blot er tre ord: "gør smarte ting" – en åben instruks der giver agenterne bredde og spillerum til selv at identificere og udføre relevante opgaver. Episoden handler overordnet om det tankeskift, der kræves for at udnytte AI-agentarbejdsstyrker strategisk og se muligheder for B2B-startups inden for området.
|
|
Grok Bot is For Real. What You Need to Know.
Nate Herk | AI Automation
Grok Bot is a new multi-agent AI platform available on Windows, Mac, and iOS that lets users spin up specialized AI agents, each with its own cloud computer, memory, and ability to communicate with other agents. Users can connect plugins like Gmail, Google Calendar, Slack, and GitHub, then set up time-based or trigger-based routines that run in the cloud even when devices are offline. The presenter demonstrates practical use cases including a morning briefing agent, a Slack media/sponsorship monitor, and a coding team where an executive assistant agent delegates software tasks to a developer agent, all set up in minutes with simple prompts. He notes the platform uses Grok models rather than Claude or GPT, making it best suited for on-the-go task management and delegation rather than deep technical development work.
|
|
What Is Context Engineering? Why It Matters for AI Agents
IBM Technology
Kontekstteknik er praksis med bevidst at strukturere og optimere den information, der sendes til en LLM eller AI-agent, for at producere mere præcise og relevante output. I modsætning til prompt engineering, der blot handler om at formulere instruktioner, omfatter kontekstteknik hele informationsmiljøet: systemprompt, brugerforespørgsel, hentede dokumenter, samtalehistorik og værktøjsoutput. En central pointe er, at mere kontekst ikke nødvendigvis giver bedre resultater – for meget eller dårligt struktureret information kan forværre ræsonnement og føre til hallucinationer. Videoen illustrerer konceptet med et sundhedsvæsen-eksempel, hvor en AI-planlægningsassistent leverer langt bedre svar, når den forsynes med klinikkens politikker, lægens tilgængelighed og patientens præferencer frem for blot en simpel prompt.
|