Nerd Snipe with Theo and Ben

Theo and Ben break down a packed week of AI releases, from Opus 5.5's insane release to Grok 4.7 falling short of the hype. They then dig into why post-training matters, where Jev, GPT-6 Sol, and Luna fit, and more!

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Timestamps:
0:00 Intro
3:18 GPT-6 Sol & Luna
14:58 Grok 4.7
38:47 Jev explained
52:48 Why model routing fails
1:05:06 Opus 5.5
1:26:17 Pricing & usage limits
1:34:23 Building games with Opus
1:41:10 Porting TypeScript to Rust
1:48:57 Agent workflows & testing

Theo & Ben break down Jacob Coxon's resignation after three years in pretraining at OpenAI and Anthropic—and his warning that both labs are racing toward AGI and gambling with our lives—along with Dario's latest blog post about pacing the frontier and how Apple's iPhone Duo could have ruined app development (if AI didn't exist).

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Timestamps:
0:00 Intro
2:53 New iPhones
22:24 Apple and Carriers
32:38 Astra’s Reliability
47:55 Codex Usage and Caching
1:19:44 Anthropic Resignation
1:38:38 Pacing the Frontier
1:40:02 Embedded AI Evaluators
1:43:10 The Hugging Face Hack
2:02:29 React Native Debate
2:09:18 Fast Inference Tradeoffs
2:12:40 Democratic AI Coordination
2:18:22 China and Global Pacing

Theo & Ben break down their latest thoughts on how Astra and Fable 5.1's releases have changed the way they work, how they interact in T3 Code, and then circle back to the latest with Codex usage limits, Gemini 3.8 Flash, Muse Spark 1.3, and the growing gap between model benchmarks and real coding workflows.

Thanks to this episode's sponsor, General Translation:
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Timestamps:
00:00 Intro
04:57 Gemini 3.8 Flash
14:48 Muse Spark & small models
22:08 AI subscriptions
34:14 GPT-6 Astra launch & pricing
56:04 Rate limits & autonomous coding
1:10:14 Fable 5.1
1:23:23 AI design & 3D demos
1:40:53 Astra vs. Fable: Which to use?

Theo & Ben break down OpenAI's latest model, Astra, and why it's their new benchmark for AI coding, computer use, and multimodal work. But it's not all good: they explain why its UI, stopping behavior, and agent reliability still create friction in real software workflows. From DEFCON puzzles to coding-agent PRs, we're comparing Astra with Fable and asking what a trustworthy OpenAI model should do next.

Thank you to PostHog for sponsoring today's episode!
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Timestamps
00:00 Meet Astra
04:03 Best Model Ever, With Catches
06:33 Reasoning and 3D Benchmarks
20:00 Astra Rebuilds Ping.gg
30:04 Instruction-Following Problems
44:47 The Uncommitted Fix Debate
01:09:37 The PR Babysitting Failure
01:22:41 Why Fable Still Wins
01:30:41 Multimodal and Computer Use
01:39:29 Astra vs. Fable Fleet Data

Theo & Ben break down OpenAI's GPT-5.6 Sol price cut, breakdown the "Ox Alpha" stealth model we now know is GLM 5.3 Flash, then take a drink every time they say "Grok" while ranking every current AI model on a tier list! What could go wrong?

Thanks to this episode's sponsor, General Translation:
General Translation: https://nerdsnipe.link/gt

Listen wherever you get your podcasts:
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Timestamps
0:00 Intro
3:30 OpenAI vs. Anthropic
14:44 Anthropic’s delayed models
20:53 Kimi K3 and open weights
30:08 The Alpha stealth model
48:26 Model tier list begins
1:09:53 GPT-5.6 Luna
1:13:09 Opus and Sonnet
1:20:25 Gemini models
1:31:15 DeepSeek and local models
1:59:58 Fable vs. Sol
2:28:46 Final rankings