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0:36ENKimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.
25:50ENKimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.
32:20ENWho Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.
2:00:51ENResample Every Token and You Can Watch the Model DecideModeration: Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
21:29ENMistral Large 4 Preview: European Open Weights, Cyber Benchmarks and Setup FrictionMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported specifications and benchmarks, then tests it in an agent coding workflow.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabModeration: Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
16:39ENDeepSeek V4.1 Flash: Low-Cost Inference Meets Mixed Results in Practical TestsMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s efficient new open-weight model, its reported benchmarks and API pricing, then tests its speed, coding and visual work. His small set of demos reveals both impressive speed and problems with simulation accuracy.
0:50ENMatthew Berman on Using Idle Computers for AI InferenceMatthew Berman · Forward FutureBerman points to Darkbloom as a way to put spare computing capacity to work on AI models, while raising the question of how far distributed inference can go.
18:57ENGLM-5.3-Flash: Ox Alpha Benchmarks, Costs and Coding TestsMatthew Berman · Forward FutureMatthew Berman reviews Z.ai’s open-weight model, compares benchmark costs and tries coding and presentation tasks against a competing model.
20:46ENOpen-Weight AI Models: Token Share, Spending and Strategic TradeoffsMatthew Berman · Forward FutureMatthew Berman examines a Vercel AI Gateway snapshot and argues that model usage and spending can tell very different stories.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models BannedModeration: Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
13:58ENGoogle’s AI Leadership Changes and the Innovator’s DilemmaMatthew Berman · Forward FutureMatthew Berman considers Google’s research history, two leadership changes and an open-model strategy in August 2026.
15:37ENMatthew Berman on Qwen3.8-Max, AI Costs and Open-Weight CompetitionMatthew Berman · Forward FutureA 2026 review of reported benchmark results and research demonstrations leads to questions about deployment economics and geopolitical dependence.
2:16:29ENFrom Two Papers a Month to Sixty: What Two Weeks in China Revealed About AI SafetyModeration: Nathan LabenzNathan Labenz argues that the familiar 'China will never slow down' line does not survive contact with the actual Chinese AI safety ecosystem.
54:57ENOpen-Weight AI: Competition, Safety and the Debate over AnthropicMatthew Berman · Forward FutureMatthew Berman argues for broad access to AI models, weighs the economics and security arguments, and responds to Dario Amodei’s position on open weights.
52:29ENClaude Opus 5: Benchmarks, Cost per Task, and Early ImpressionsMatthew Berman · Forward FutureMatthew Berman reacts to the model launch, examines reported benchmark and pricing data, and explains why the cost of completing a task matters more than token price alone.
16:09ENHow to Compare AI Task Costs and Split Coding Work Across ModelsMatthew Berman · Forward FutureMatthew Berman compares token prices with benchmark task costs and proposes a planning, execution and review workflow using different models.
27:13ENKimi K3 and the Debate Over Open-Weight AIMatthew Berman · Forward FutureMatthew Berman examines model economics, competition and security arguments after Moonshot AI’s Kimi K3 announcement.
12:12ENKimi K3: Coding Benchmarks, Cost and the Open-Weight AI RaceMatthew Berman · Forward FutureMatthew Berman examines Kimi K3’s early benchmark results, its cost per task and a Rubik’s Cube simulator, while weighing their limits.
1:03ENReported Chinese AI Export Discussions and a Strategic DebateMatthew Berman · Forward FutureMatthew Berman considers reported restrictions on overseas access to Chinese AI models and questions whether limiting their distribution would serve China’s interests.
1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipModeration: Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
0:42ENMatthew Berman's Concern About Access to Frontier AIMatthew Berman · Forward FutureIn a short commentary, Berman argues that restricted model access could disadvantage startups and slow US innovation.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.Moderation: Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
1:42:54ENAI and Cybersecurity: Matthew Berman’s Warnings, Predictions and Open QuestionsMatthew Berman · Forward FutureA livestream reviews reported AI-assisted threats and debates whether stronger defensive models can keep pace with attackers.
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