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Signal or Noise
We run through the week's AI headlines and make the call: is this actual signal worth paying attention to, or just noise clogging the timeline?
A human-first AI podcast.
Real talk. Unpopular opinions. No hype. Two builders cut through the AI hype cycle every week — and call it like it is.
Hosted by Oscar Gallo & Matt Wozniak
Now playing · Latest episode
Ep 23: AI is going to kill us all
EP 23 · September 15, 2026 · 71 min
We’re the humans in the loop.
In machine learning, “human in the loop” means a human who provides oversight and feedback in an automated system. That’s the lens of this show: AI is powerful, but humans aren’t leaving the loop. Not yet. Maybe not ever.
This isn’t another “AI is going to change everything” podcast. It’s for builders, operators, and the AI-curious who are tired of breathless hype, doomerism, and surface-level news recaps with no original thought.
What’s in each episode
Every week: one fixed, one rotating.
ROTATING
Ship It or Skip It
A real use case or product idea lands on the table. We debate whether it's worth building now or if the tech isn't there yet.
ROTATING
Explain It to My Client
Take a complex AI concept and explain it the way you'd actually explain it to a non-technical stakeholder. No jargon allowed.
ROTATING
Stack Check
One tool, library, or workflow change we actually adopted this week. No sponsorship energy. Just what's in the trenches.
Oscar Gallo
AI Engineer & Entrepreneur
AI Engineer and entrepreneur. He lives in the intersection of engineering and businesses.
Matt Wozniak
Serial Builder & Relentless Executor
Serial Builder and relentless executor. He comes from the lens of what works and what doesn't.
EP 18Aug 11, 2026 · 108 minStack Check
Meta launched Muse Code, a terminal coding agent designed to work across large repositories, coordinate background agents, recover after crashes, and keep going for hours. Is this a better coding system or simply more automation to review?
Oscar and Matt look past the model benchmark and examine the operating system around Muse Code. Persistent workers use isolated worktrees. A local event log records model calls, approvals, edits, and tool use. Meta says one kernel-optimization run made more than 1,000 tool calls over as long as 24 hours.
The core question: what changes when agents persist for hours, survive crashes, and coordinate other agents without waiting for you?
Signal or Noise
The week’s AI headlines, filtered.
- SIGNALMeta released Muse Code and Muse Spark 1.2
Released August 5 in beta for macOS and Linux, Muse Code is a terminal coding agent built for long jobs across large repositories. It launches persistent background agents, splits work across isolated worktrees, and leaves your working copy untouched until changes are ready to review. The durability is the real advance: an append-only local event log of approvals, model calls, edits, and tool use lets it reconstruct a session after a crash instead of starting over. Meta co-trained Muse Spark 1.2 with the harness and demonstrated more than 1,000 tool calls across GPU-kernel optimization runs lasting as long as 24 hours.
- SIGNALOpenAI published ten Astra-generated results in math and theoretical computer science
On August 1 OpenAI published ten results spanning high-dimensional geometry, coding theory, group theory, operator algebras, quantum complexity, lattice cryptography, and extremal combinatorics. An internal version of Astra generated the arguments, humans prepared the manuscripts with the model, and the model formalized each argument in Lean. OpenAI shipped a 249-page paper, reasoning walkthroughs, and machine-checkable certificates, and estimates the successful runs cost about $2,000 in tokens. Lean can confirm a proof follows from its stated assumptions; it cannot confirm the formal statements match the original research questions or that the claimed novelty survives expert review. Signal pending outside review.
- SIGNALDeepSeek moved its updated V4 Flash API into public beta
V4 Flash entered public beta on July 31 with the same 284 billion-parameter mixture-of-experts architecture as the preview and 13 billion active parameters, but new post-training aimed at agent work. It supports a 1 million-token context window, thinking and non-thinking modes, tool calls, and both OpenAI-compatible and Anthropic-compatible APIs. Listed pricing is $0.14 per million uncached input tokens and $0.28 per million output tokens, with published setup guides for Claude Code, GitHub Copilot CLI, and OpenCode. Cheap enough to test inside a real agent loop without rewriting the harness.
- SIGNALThe US frontier-model review framework reportedly excludes open models
Axios reported on August 4 that the White House finalized a voluntary pre-release review framework under the June 2 executive order, including a classified cyber benchmark and up to 30 days of government access to a covered model before it reaches other trusted partners. Per people briefed on it, a covered model must be closed-source, state of the art, and present national-security risks, and open models are excluded. Neither the framework text nor the capability thresholds are public, so the carve-out comes from reporting on a closed industry briefing rather than a published document. The policy creates different review paths based on how weights are distributed.
- SIGNALDemis Hassabis left the DeepMind CEO role as Jeff Dean exited Google
Google announced its largest AI leadership change since the Brain and DeepMind merger on August 5. Hassabis leaves the Google DeepMind CEO role but stays at the company as DeepMind chair and Alphabet chief scientist while continuing to lead Isomorphic Labs. Jeff Dean is leaving after 27 years to start a company focused on AI and scientific discovery, with Google investing. CTO Koray Kavukcuoglu becomes senior vice president reporting directly to Sundar Pichai, and no replacement CEO was named. Signal, but not yet a product signal: the evidence is research retention, Gemini release quality, and whether decisions move faster.
Stack Check
Each host brings one workflow builders can use now.
Oscar: Codex and ChatGPT in one desktop app
Matt: Warp as the terminal layer for Codex CLI and agent-heavy work
Hot takes
Two opinions, no disclaimers.
Oscar“A coding agent is not ready for long, unattended work unless it can reconstruct every action after a crash. Durable state and an audit trail matter more than another benchmark win.”
Matt“Everyone is calling this a light touch. Voluntary, relax. I have never seen a government program stay the size it started. Thresholds move. Thirty days becomes sixty. And the countries we are actually racing are not running this process and never will. We are building a very careful system for arriving second. Every day of review is a day of lead, and nobody is counting the bill.”
EP 17Aug 4, 2026 · 63 minGuest Deep-Dive
AI made engineers faster. It did not make companies faster at deciding what to build. That is now the hard part.
Mike Lyons and Greg Pfister of KaiRise join Oscar and Matt. The conversation starts with a $4.3 million voter registration system that shipped on time, on budget, and on scope. Users still rejected it. The team built what was requested, but not what people needed.
AI makes that risk bigger. Teams can produce more software in less time. Leaders still need to set direction. Product teams still need to understand customers. Someone still has to decide when to stop.
Guest Deep-Dive
What we cover with Mike Lyons and Greg Pfister.
Engineering is 20% of delivery
If writing the code was never the whole job, making it faster does not make the company faster. The slow part moved to everything around engineering — direction, decisions, approvals.
Five-year plans meet weekly shipping
Annual budgets and long approval chains were designed for teams that delivered slowly. When delivery compresses, the planning cycle becomes the constraint.
Big-A Agile versus lowercase-a agility
Formal Agile roles are in decline. The underlying principles — short feedback loops, working software, real customer contact — did not stop mattering.
Prototypes instead of requirement sessions
A live prototype settles in one meeting what a requirements document argues about for a month. Building got cheap enough to make showing the default.
What is a story point when agents write the code
Estimation assumed human hours. Points, lines of code, and token usage all measure output, and none of them prove a customer got value.
The skill developers need now: knowing when to stop
The scarce ability after AI is not producing more. It is recognizing the point where more code stops improving the outcome.
The organizational problems AI cannot solve
Unclear ownership, misaligned incentives, and teams shipping features nobody asked for are structural. No model fixes them.
Sources referenced
Previously
They Want to Ban Kimi K3
EP 16Jul 28, 2026 · 116 minShip It or Skip It
Kimi K3 got hot enough to trigger a U.S. ban debate. If Washington restricts a model you can download and run yourself, is that national security, closed-model protection, or both?
Oscar Gallo and Matt Wozniak cut through the week's real AI stories.
Signal or Noise
The week’s AI headlines, filtered.
- SIGNALKimi K3 demand forces Moonshot AI to pause new subscriptions as U.S. restrictions on Chinese models surface
- SIGNALOpenAI says an advanced agent escaped a security test and breached Hugging Face infrastructure
- SIGNALAnthropic releases Claude Opus 5 and GitHub adds it to Copilot for long-running coding work
- SIGNALMeta AI now plans, connects to email and calendar, researches topics, and completes configured tasks in select markets
- SIGNALThe 50-company open-weights letter, and why Matt thinks OpenAI is the only signature that costs its signer something
Ship It or Skip It
we debate two AI business ideas.
Agent containment lab for autonomous tools
Independent referee for long-running code agents
Previously
Your CEO Says “We Need AI.” Now What?
EP 15Jul 21, 2026 · 69 minDear Human in the Loop
Your CEO says, “We need AI.” Before you hire an AI engineer or buy another tool, find one painful process worth fixing.
Episode 15 is our first Dear Human in the Loop, an advice episode for founders, engineering leaders, developers, and decision-makers trying to make practical AI calls.
Dear Human in the Loop
This week’s questions from the inbox.
- Q1. My CEO said, “We need AI.” Where do I start?
Start with problems, not prompts.
- Q2. Should an AI engineer be my startup's first technical hire?
Hire for your biggest business problem, not the trendiest job title.
- Q3. My developers do not trust AI. Should I push them to use it?
Show developers real value and give them time to learn. Trust beats mandates.
- Q4. Which skills will still matter five years from now?
Learn engineering fundamentals, system design, product thinking, communication, and critical thinking. Model quirks will expire.
Sources referenced
EP 14Jul 14, 2026 · 92 minNo Jargon Required
Apple says OpenAI built its hardware program with stolen trade secrets. OpenAI denies any interest in Apple's secrets. We make the Signal or Noise call.
Signal or Noise
The week’s AI headlines, filtered.
- SIGNALApple sues OpenAI over alleged trade-secret theft
- SIGNALMeta releases Muse Spark 1.1 and opens the Meta Model API to developers
- SIGNALOpenAI releases GPT-5.6 across ChatGPT, Codex, and the API
- SIGNALSpaceXAI releases Grok 4.5 for coding and agentic work
- SIGNALAnthropic and OpenAI trade usage resets during the GPT-5.6 launch
No Jargon Required
we explain two terms a decision-maker needs this week.
Multi-agent orchestration: when several AI workers help and when they multiply failure
Model routing: how a product switches among fast, cheap, and capable models
Hot takes
Two opinions, no disclaimers.
Oscar“The Apple lawsuit is a warning for every AI company hiring from a competitor. Talent does not arrive empty-handed. If your onboarding process does not separate experience from confidential material, your product roadmap can become evidence.”
Matt“I don't care if your model is as good as Fable. If it's close, and it's faster, and it's cheaper — it wins. The whole industry is selling you a god-tier model like it's the finish line, but with orchestration and model routing, you don't need one genius doing everything. You need a smart conductor and a cheap, fast bench. The frontier model is becoming the part you use least — and the teams still paying premium tokens for every keystroke are going to feel really dumb in a year.”
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