Went on one of the biggest AI channels in the world to reveal two things: 1) How we're killing consulting. It needed to be taken out back, and rebuilt from the ashes. 2) The MetaHarness - an AI-native workflow + SDLC we used to 10x productivity Def watch the full video (@DavidOndrej1/@dan_zakon crushed it), but here are the highlights: 1) How to make a slow company AI-native - Meet the company where it actually is: systems, people, and where they sit on the AI journey. - A 2026 consulting business for Fortune 2000s has to be a full-stack partner, not a deck shop. - Old model: consultants take a problem, sit on it for months, deliver a 150-page deck, say peace, leave the client to implement. - Post-AI: you have to touch the whole chain. Strategy, change management around the tech, and forward-deploying into the company. - For a lot of Fortune 500s, the first move is teaching the C-suite Claude CoWork/Claude Code or Codex. - The job is to drive friction as close to zero as possible. 2) How we're killing & reviving consulting - Me and @ArmanHezarkhani did not dream of being consultants. - The vision: modern-day Bell Labs - My bar: if I'm not home with my wife and daughter, the work has to be worth it. Worth it = frontier problems with the smartest, most driven people you have worked with. - Arman read The Innovators in school (Babbage and Ada Lovelace through mobile revolution). The book’s lesson on innovation centers: Bell Labs / the transistor, DARPA / the internet, Xerox PARC. The raw materials are always brilliant people given autonomy, plus direction, distribution, and resources. - We did not want to raise hundreds of millions on day one. Helping the biggest companies solve their hardest AI problems cashflows the business, tells us what actually hurts, and supplies the materials for an applied AI lab that incubates consequential post-AI tech. - Consulting is a dirty word. It's growth & innovation as a service. - Classic consulting pitch: go in, problem, the answer is often firing people, deck, walk away, never see the fruit. That is why great technical talent never went there. - Reposition: messy, important problems, no answer, an A-team for innovation and implementation, actual ROI. - Consulting by-design gives engineers diversity of work without job-hopping. “ADHD is satisfied, same company.” 3) Singleplayer vs. multiplayer AI - Single-player: swap Google for GPT/Claude on the same query. Zero behavioral change. Still valuable. - Enterprise version of that: sign a $5–10M token agreement with OpenAI or Anthropic, get secure Codex/GPT or Claude Code/Co-work, put SOTA models in front of people, teach daily use. Most companies should start here. No compounding. - Multiplayer: reinvent a horizontal process so leverage hits a whole function or the whole company. - Examples: an SDR/sales agent that gives sellers back time from logistics so they talk to clients. Data engineering when an “AI problem” is actually a data-readiness problem. - Exponential, messy, painful value is multiplayer. 4) Breaking down the MetaHarness - Engineers have always been methodical about files. Knowledge workers are playing catch-up. Tenex is taking it further: extremely prescriptive context for coding agents. - Old SDLC: human-to-human coordination via Agile ceremonies (standups, retros, a calendar full of meetings). - New SDLC: humans, other humans, and agents. Agents are the main character doing the work on the ground. - Written-first culture matters more than ever. Markdown briefs humans and agents. - Agents can hold more coherent information than humans, so you can write bigger blueprints further in advance than Agile ceremonies ever allowed. - Concrete markdown artifact types on every project. Agents pull the right context at the right moment and keep it in sync. Dan calls it a “software machine.” - The problem the harness is solving: agents give speed that did not exist. Longer tasks increase entropy and diversion from plan. Minimize entropy without giving up speed.

Couldn't help myself. Forward deployed engineer or AI engineer sounds dry as cardboard, so I asked my team to come up with the most creative job titles describing what they do... The current list: - Transformer tamer - LLM whisperer - Hallucination exorcist - Token daddy - Weights guy - Human out of the loop - Latent freak - Gradient guru - RL gym junkie - Inference dealer - Nothing-but-the-harness - Benevolent prompt injector - The metaharness - Context window's context window Who can top this?

Quick lookback on technical AI themes of 2026: (dataset: daily ainews title stories) Jan: - Ralph Loops - RLMs - Filesystems are all you need - X LLM recsys open sourced - X Grok Imagine #1# Video model - Anthropic blocked OpenCode / OpenClaw - MCP Apps - Moltbook - Apple picks Gemini for new Siri - Notable models: Kimi K2.5 - Fast inference: OAI x Cerebras partnership - Big raises: xAI @ $230B + SpaceX, OpenEvidence @ $12B Feb: - Codex App launch - Claude Code 1 year anniversary - Context Graphs - Fast Inference: Taalas 17k tok/s/user Llama 3 8B - Anthropic vs Deepseek/Moonshot/Minimax distillation accusation - Notable models: Opus/Sonnet 4.6, 5.3 Codex and Codex Spark, Qwen-Image 2.0, Seedance 2.0, GLM-5, Gemini 3.1 Pro and Deep Think, Nano Banana 2 - Big raises: OpenAI @ $840B, Anthropic @ $380B, ElevenLabs @ $11B, Cerebras @ $23B Mar: - T3 Code - Autoresearch - Claude Code source leak - Claude Cowork Dispatch - OpenAI buys Astral - Cloudflare-Next.js "Slop Fork" - Meta buys Dreamer - NVIDIA GTC: OpenClaw, Vera CPU, 1T sales backlog, "The Inference Inflection" - ARC-AGI 3 introduced, SOTA models are 1% - Notable models: GPT 5.4 unifies chat and codex, Gemini 3.1 Flash-Lite/Live, Nemotron 3 Super, Composer 2, MiniMax 2.7, Arcee’s Trinity-Large-Thinking - Big Raises: Yann LeCun AMI Labs $1B seed Apr: - Claude Design - OpenAI Symphony & Harness Engineering discourse - @LilianWeng writeup later - OpenAI and Microsoft break up - Tokenmaxxing fears - Codex Superapp - AIE Europe, AIEi Miami - Hermes Agent gains traction w/ Manim skill - Cyber: Anthropic Project Glasswing - Notable models: Claude Mythos "Capybara", Opus 4.7, Muse Spark, Gemma 4, Kimi K2.6, GPT-Image-2, GPT 5.5 "Spud", DeepSeek v4 Pro/Flash Base/Instruct on Huawei Ascend, Poolside Laguna XS.2, Grok 4.3 - Big Raises: not much but Anthropic $19B->$30B ARR in one month... May: - Cyber: OpenAI Daybreak, Tanstack Mini-Shai Hulud - FDE: OpenAI DeployCo, Ode by Anthropic - OpenAI solve Erdos planar unit distance problem - OpenAI deprecates finetuning - "How to land a job at a frontier lab" - Anthropic rents Colossus I from SpaceX for $5B/yr - Thinking Machines Interaction Models (still underrated!!!) - Notable models: GPT-Realtime-2/Translate/Whisper, Gemini 3.5 Flash, Omni, Antigravity 2.0, Opus 4.8, Cosmos 3, Minimax M3 - Big Raises: Fireworks @ $15B, Baseten @ $11B, Cognition @ $26B, Cerebras $60B IPO, Anthropic @ $965B Jun: - Claude Tag - OpenAI Jalapeno - "The Untrainable" - @saranormous - Loopcraft: The Art of Stacking Loops - Metaharnesses discourse? Flue v Eve - Zai Prof @jietang predicts Open Fable by Dec - "SpaceX is a $28B/yr neocloud" - @jaminball - Anthropic-Fable White House issue (resolved) - Notable models: GPT 5.6 Sol/Terra/Luna, Fable 5, Sonnet 5, MAI-Thinking-1 and MAI Family, Nemotron 3 Ultra, GLM 5.2, Reve 2/ Ideogram 4 - Big Raises: SpaceXai buys Cursor for $60B, Supabase @ $10B Jul: - AIE World's Fair 3 - OpenCode 2.0 - Cyber: OpenAI-HuggingFace incident - Chinese Model ban discourse - GPT 5.6 Luna 80% price cut - Notable models: Grok 4.5, Opus 5, Muse Image, Thinky Inkling, Kimi K3, BFL FLUX 3, DeepSeek-v4-Flash-0731 - Big Raises: Aug: - EU AI text watermarking - Microduck - Megakernels - Memory prices up 500% in 12 months - How to steal a Reasoning Trace - Grok @bot! - OpenAI shuts off Cursor - Fast Inference: AMD buys Taalas - @andrewng stars AI Engineering series - Hot Chips: Jalapeno, Cerebrase CS-5, Groq 3, Apple M6 - Demis steps down from CEO, & Dean et al leave - Notable models: Qwen 3.8 Max, Muse Spark 1.2, Muse Glimmer, Grok 4.6, Gemini 3.7 Flash, GLM 5.3, @Fal H3 Max Live - Big Raises: Stripe buys OpenRouter $7B, Poolside $12B reverse-execuhire to NVIDIA, NVIDIA buys HuggingFace for $13B Sep: - AIEi Paris - OpenAI Navier-Stokes singularity - collusion . wiki - Yegge shuts down Gas Town - AEF-1 standard for Third Party Evaluators - Meta Muse App launch. Also new glasses and Charm - OpenAI blocks Cursor - OpenAI Dots - Opus 5.5 explainer videos - Pi 1.0 & Pi Durable - Notable Models: Fable 5.1, Mythos 5.1, Opus 5.5, GPT-6 Astra, GPT Image 2.5, DeepSeek v4.1-Flash, @typesafeai Jev, MiMo-v2.6-Pro new Open SOTA, GPT 6.1 Sol & Ultrafast & Decisions API, Gemini 4 Argon - Big Raises: Cognition @ $48B, Mistral @ $24B, AMD buys World Labs for $8.2B Oct to date: - AIE NYC - @dhh stops using Rails - ArtCraft clones Adobe suite - Interpretability vs Polysemanticity - OpenAI Quasi-Riemann-Hypothesis & 722 Math - Notable Models: Reflection Beam, Claude Haiku 5.5, Muse Wonder & Wonder Full reach Opus 5.5 tier - Big Raises: TypeSafe $7.5B 3 weeks after launch, Modal @ $16B (this was done for research purposes for the keynote in Nov, just sharing notes in case helpfuil)

What is a meta-harness and why do coding agents need one? 👇 Elise Gonzales joins @codebasicshub to explain how Omnigent provides a shared layer across coding harnesses, making it easier to switch and compose harnesses, apply guardrails outside the agent, share context across workflows, and collaborate through agentic pair programming. 🎥 Watch the full conversation: #Omnigent# #AIAgents# #OpenSource# #CodingAgents# @dpcodebasics

New video: @leonvz demos Omnigent running Claude Code and Codex in one open source meta-harness. He plans a 3D game, forks into Debby for a multi-model debate, then hands off to Polly to delegate the build. Policies, MCP, and prompt injection guardrails too. 👇 🎥 Watch: #Omnigent# #AIAgents# #OpenSource#

Check out this hands-on walkthrough of Omnigent from Daniel Beach: what a meta-harness actually is, without assuming you already live in this stack. The blog covers installing Omnigent, connecting Claude Code and Copilot, and the pieces that matter in daily work. 🔹 One common layer over your coding harnesses, plus a Web UI next to the terminal 🔹 Policies for cost and safety, including asking before git and bash 🔹 Polly for multi-agent planning and research 👉 Read the full post: #Omnigent# #AIAgents#

Last night at Open Lakehouse + AI in Paris 🇫🇷 Aravind Segu and Edwin He presented Omnigent: A Meta Harness for AI Agents covering why adopting agents is easy and operating them isn’t, then a live demo. Thanks to everyone who came out! 🗓️ Upcoming Omnigent events:  #OpenLakehouse# #Omnigent#

Nimble now runs natively inside Omnigent, Databricks' open-source agent meta-harness. See en example of what that looks like with our new cookbook: an agent that decides which API to use, then ships the integration. A Nimble Web Search Agent researches each candidate against one shared schema, returning per-field citations and confidence. A Claude head and a GPT head debate the evidence. A coding agent implements the winner on a reviewed branch. Cookbook: Docs:

Ever tried getting Claude Code and Codex to actually work together without playing copy-paste between terminals? @leonvz takes Omnigent, our new open-source meta-harness, for a spin to show how multiple coding agents can share sessions, rules and security policies in one system. He demos forking work across agents, multi-agent review and debate with Debby, and splitting implementation across subagents with Polly. Watch the full walkthrough:

Join us at AI Infra Nights x Autonomous Engineering and catch Dhruv Gupta's session, Omnigent: A Meta-Harness for AI Agents 🙌 🗓️ Thurs, Sept 24 🕜 6:00 PM PT 📍 @databricks SF Space is limited 🎟️ Register: #Omnigent# #AIAgents# #OpenSource#