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1:25:10ENWriting Code Is Only 30% of the Job. CodiumAI Wants the Other 70%Host Nathan LabenzItamar Friedman argues that completion is already a commodity, that code integrity is where AI still lags, and that flow engineering—not fancier prompts—is how GPT-4 doubled its score on coding contests.
13:57ENMistral Large: A 2024 Review of Coding, Reasoning and Safety ResponsesMatthew Berman · Forward FutureMatthew Berman reviews Mistral’s launch claims and tests the model with a small set of prompts.
11:14ENGemini Advanced and Ultra 1.0: A First Look and Hands-On TestMatthew Berman · Forward FutureIn February 2024, Matthew Berman reviews Google’s Bard rebrand and tests its new paid Gemini experience on coding, logic and image recognition.
46:36ENAfter a decade of code search, Sourcegraph’s CTO says the coding-AI fight is outside the modelHost Sarah Guo, Elad GilBeyang Liu on Cody, why keyword search still does most of RAG, and why CS fundamentals get more valuable as AI compresses the middleware.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Host Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
1:15:39ENAfter 20 Years of Frontend, v0 Wrote Better Code Than Guillermo Rauch DidHost Nathan LabenzThe Vercel CEO tells Nathan Labenz why every product needs ghost text, why Software 2.0 will ossify the old stack, and why betting against open source is still the expensive mistake.
7:48ENTesting Dolphin 2.2 Yi 34B on Coding and Reasoning TasksMatthew Berman · Forward FutureMatthew Berman runs a quantized Yi fine-tune through informal coding, writing and logic prompts in November 2023, with uneven results.
32:48ENFourteen People, $200 Million of Compute, and the Reliability Bottleneck for AgentsHost Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.
7:02ENConnecting AutoGen to a RunPod-Hosted Model with Text Generation WebUIMatthew Berman · Forward FutureIn a 2023 walkthrough, Matthew Berman connects AutoGen to Dolphin 2.1 Mistral 7B through an OpenAI-compatible API and tests a small coding task.
1:32:41ENThere are maybe 5,000 people who can train frontier models. The rest need AI engineersHost Nathan LabenzSwyx of Latent Space tells Nathan Labenz how a new job is forming, why OpenAI will Sherlock its developers, and what still does not count as an agent.
7:32ENMageGPT Generates a Wasp To-Do App: A 2023 DemonstrationMatthew Berman · Forward FutureMatthew Berman uses a Wasp-sponsored app generator to build, run and deploy a basic full-stack to-do app.
2:17:30ENGemini Became the Daily Driver. A GPT-5 Worthy of the Name Would Make Agents Work.Host Nathan LabenzZvi Mowshowitz returns to Cognitive Revolution to unpack Gemini 1.5, Sora, superalignment, sleeper agents, and the live-player map of frontier labs.
29:38ENA Beginner Just Shipped a $250K Business. Professionals Got 30 Percent.Host Sarah Guo, Elad GilReplit CEO Amjad Masad on Ghostwriter, why a 3-billion-parameter model was enough, and the agents he wants earning bounties while you sleep.
9:59ENOpen Interpreter: A 2023 Local Code-Execution WalkthroughMatthew Berman · Forward FutureMatthew Berman installs Open Interpreter, analyzes local files with GPT-4 and tries a local Code Llama setup, with mixed results.
6:11ENRunning WizardCoder Locally with Text Generation WebUIMatthew Berman · Forward FutureMatthew Berman walks through a 2023 setup for a quantized Code Llama-based coding model, from the Python environment to a simple test prompt.
10:20ENPhind Code Llama 34B vs GPT-4: A 2023 Python Coding TestMatthew Berman · Forward FutureMatthew Berman compares a quantized Phind fine-tune with GPT-4 on games, short functions and refactoring, with mixed results.
1:39:53ENUnder 100 People, 24 Million Users: Replit's Bet on Artificial Developer IntelligenceHost Nathan LabenzMichele Catasta, Replit's new VP of AI, lays out a generate-execute-learn loop he will not call AGI, a 2x-to-1000x productivity ladder, and a keystroke-level data advantage.
1:45:46ENReplit's $10 AI Programmer Already Pays for Itself. The Ambition Is Bots Hiring BotsHost Nathan LabenzTyler Angert, Replit's first product designer, explains Ghostwriter, a virtual developer, and why the company calls itself the perfect substrate for AGI.
12:20ENTesting UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.
7:37ENSmol Developer: Generating a Small Python Project From a PromptMatthew Berman · Forward FutureMatthew Berman walks through a 2023 project generator, from a simple Ruby example to a multi-file ChatGPT API program.
59:38ENRichard Eisenberg on Dependent Types, OCaml, and AI-Assisted ProgrammingYaron Minsky, Richard Eisenberg · Jane StreetHow type systems can express correctness and performance, and why precise, readable specifications still matter when AI writes code.
41:40ENEvery 10 Milliseconds Cost Copilot 1% of CompletionsHost Sarah Guo, Elad GilGitHub Copilot's chief architect, Alex Graveley, walks through how a leftover code model with a sub-10% test pass rate became a product developers would not quit — and what he is building next at Minion AI.
58:08ENAmjad Masad on Replit, Company Culture and AI-Assisted SoftwareGuest Amjad Masad · Host Harry StebbingsThe Replit founder discusses hiring, developer productivity and the opportunities and risks he saw in AI in 2023.
32:48ENA Hundred Million to Train, a Penny to InferHost Nathan LabenzIn a February 2023 roundtable, two founders argue on mic over whether AGI ends humanity — and who actually captures the value if it does not.
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