Running a Software Repository with Grok Bot Agents
Ray Fernando turns Grok Bot into a repository coordinator that delegates coding work, tracks pull requests and uses specialized agents to keep delivery moving.
Agent Workflows
Videos about designing repeatable processes in which AI agents plan, act, review results, and hand work between stages. 38 videos.
Eight Practical Grok Bot Agent Workflows
Alex Finn organizes Grok Bot as a team of named cloud agents for email, coding, content, research and operations, with a chief-of-staff agent coordinating their work.
Hermes Bot Multi-Agent Workflow Review
Alex Finn finds that Hermes Bot makes specialized agent teams flexible and affordable, while Grok Bot still offers smoother orchestration and stronger built-in workspaces.
Grok Bot: From Beginner to Expert in 25 minutes
Grok Bot turns a simple chief-of-staff interface into a coordinated cloud workforce by delegating work to specialized agents with separate context, accounts and routines.
Claude Code New Features, Explained
Claude Code works more like a dependable AI employee when a project supplies shared context, scoped tickets, review standards, testable feedback loops, recurring routines and explicit permission boundaries.
Grok Bot Is The First AI Agent You Just Install. Is It Worth $200?
Grok Bot makes an agent workspace unusually easy to install and operate, but its cost, broad computer access and uneven reliability require careful evaluation.
My top secrets to running an AI Agent Workforce
Proactive AI workforces need goals, broad but accurate context, permission to act within fixed risk limits and watchdogs that identify friction without making the human manage every task.
Three OpenAI Engineers Shipped A Million Lines. Your Ten-Hour Agent Run Starts Here.
Long-running coding agents work better when people progressively shape context through durable instructions, current state, project maps and review checkpoints.
Nemotron 3.5 Lightning First Test - NVIDIA's Newest Open Model
Bijan Bowen finds Nemotron 3.5 Lightning more convincing as a fast, long-context agent model than as a polished coding or visual-development model.
Every AI Tool You Need to Be Using in August 2026
Alex Finn recommends choosing AI models and interfaces by task instead of expecting one system to handle planning, coding, design, mobile delegation, local work and collaboration equally well.
Ex-Uber dev explains his Multi-Agent Workflow
AI agents become more useful teammates when a whole team shares their context, tools, memory and collaboration surfaces instead of operating isolated personal agents.
Cloudflare Will Make 1,000+ AI Millionaires
Greg Isenberg argues that an agent-readable, metered web creates practical opportunities in niche data, agent-ready content and narrowly useful expert tools.
29% Of Your Employees Are Sabotaging Your AI Rollout. The Fix Is 3 Things.
Successful AI adoption requires a credible commitment to people, a focused pilot tied to business value and a deliberate path from technical learning to organization-wide change.
ChatGPT Voice is the most powerful AI feature of 2026. Here’s how to master it
Alex Finn uses ChatGPT Voice as an orchestrator that delegates work to stronger agent threads, coordinates devices and keeps remote tasks organized.
Your Chatbot Hallucinated in 2024. Your Agent Lies in 2026.
Agents may satisfy the visible form of a task while missing its intent, so their work needs independent checks, explicit quality standards and achievable access boundaries.
Ex-NASA dev reveals his Agentic Engineering Workflow
Maintainable agent-written software requires humans to decide product intent, architecture, program structure and testable vertical slices before agents implement the code.
Meta Muse Code Is HERE – Spark 1.2 & Meta’s NEW Coding Agent!
Bijan Bowen finds clear improvement in Muse Spark 1.2 and useful Muse Code agent modes, but uneven results and high test cost limit the value case.
Marketing Agents Masterclass (GROW your startup)
Marketing agents work best as narrow code-first systems that connect intent signals, enrichment, outreach, follow-up and content feedback while reserving model inference for real judgment.
Qwen 27B Multi-Agent Flight Simulator Test
Multi-agent coding worked best when an orchestration layer split the job into milestones, reviewed intermediate work and recovered from context failures, while the same local model working alone did not finish the application.
Why Graph Engineering Improves Claude and Codex Workflows
Graph engineering turns a vague one-shot prompt into an explicit sequence of planning, parallel research, criticism, synthesis and human review that can be tested and improved.
Don’t use AI Agents without using these 8 skills
David Ondrej's most useful agent skills turn recurring practices into reusable instructions for safety, isolation, delegation, guided setup, decision review and reliable long-running work.
If OpenAI And Anthropic Are Discouraging You, You're Probably A Level 1 Builder.
AI builders become more resilient as they move from attachment to one idea toward customer insight, distribution, a defensible domain thesis and accurate forecasts of new capabilities.
Jack Dorsey's Buzz Has Left Me Completely Speechless...
Buzz brings people, AI agents, repositories and local compute into one shared workspace so teams can collaborate with a common context instead of isolated assistants.
Paste This Into Claude, Never Hit a Token Limit Again
Nate B Jones shows that long AI sessions become cheaper and more reliable when users stop resending stale history and carry forward only accepted, task-relevant context.
Jack Dorsey's Buzz: Clearly Explained (and how to use it)
Greg Isenberg and Vince Canger present Buzz as an open shared workspace where people and agents can collaborate, build software and retain context across model changes.
The Greatest AI Tool Ever?
Alex Finn uses ChatGPT Voice as a mobile command center that reviews active projects, delegates work to separate agents and reports progress without continuous screen use.
Marketing Agents Are Too Good Now
Greg Isenberg and Cody Schneider explain that effective marketing agents need unified business data, constrained decision loops and continuous performance feedback rather than one-off automations.
Agentic Engineering, explained by a 10x developer
David Ondrej and Thorsten Ball argue that stronger coding agents shift software work from typing and model micromanagement toward product judgment, clear context and asynchronous verification.
I Gave An AI Agent My Support Inbox. It Cut The Work By Two-Thirds.
Nate B Jones shows how AI can reduce support volume by tracing recurring complaints to root causes, gathering context and preserving human approval for consequential actions.
Claude Opus 5 DESTROYS Fable 5
Opus 5 wins most difficult coding comparisons, but verbose behavior and a weaker coding harness can make it less efficient as an everyday default.
Most Valuable Skill of 2026: Managing AI Agents
AI-native software teams work best when people manage parallel agents through clear context, secure boundaries, automated testing and deliberate decision points.
Buzz Agent-Orchestration Limits
Buzz makes multi-agent collaboration visible, but its manager-worker behavior is mostly prompt-driven and lacks the enforced state, stopping and recovery rules needed for reliable orchestration.
Prompting Is Dead. Here Is How You Create Loops
Reliable agentic coding comes from a repeatable loop that separates specification, implementation and review while keeping each change visible and controllable.
GLM 5.2 vs Kimi K3: Which Open Source Model Wins?
Pat Simmons finds Kimi K3 substantially stronger and often more efficient for complex coding and design, while GLM 5.2 remains the cheaper choice for straightforward research writing.
FDE: The $1M/Year AI Job Explained
Greg Isenberg and Vasuman Moza explain that forward deployed AI engineers create value by mapping real workflows, choosing where models belong, validating outcomes and integrating reliable agents into existing systems.
Do these 5 things in Claude Fable 5 NOW
Alex Finn recommends using Fable 5 to rethink recurring workflows, build personal context, propose multiple directions, delegate browser tasks and reserve scarce high-end usage for judgment.
L8 Principal's Agentic Engineering Setup (just copy him)
David Ondrej and Kun Chen show how one supervising agent can coordinate parallel workers, escalate ambiguous decisions, validate generated code and expose services through agent-efficient interfaces.
Codex vs Fable: Which AI Agent Picked the Better Problem?
Nate B Jones finds Fable stronger at identifying strategically valuable problems while Codex is more dependable at executing bounded tasks, suggesting teams should separate problem discovery from implementation.