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0:22ENDeepSeek's Memory Use: A Short Claim About KV CacheMatthew Berman · Forward FutureMatthew Berman links lower cache memory use to serving more tokens, but the excerpt does not state the comparison behind its figures.
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.
0:41ENCould Faster AI Agents Shift More Work to the Cloud?Matthew Berman · Forward FutureMatthew Berman reflects on a conversation about faster inference and local-computer limits. Whether the CPU is a bottleneck depends on the particular agent workload and execution environment.
0:48ENMatthew Berman on Earning From Idle Mac Compute With DarkbloomMatthew Berman · Forward FutureA proposed network of Apple Silicon Macs could serve AI inference, but the earnings shown are projections that depend on hardware, availability and demand.
13:09ENAI News: Ultrafast Inference, Claude Watermarks and New ModelsMatthew Berman · Forward FutureMatthew Berman reviews an ultrafast coding preview, text watermarking, Grok Bot, open-weight models and Computer History.
1:24ENMatthew Berman on Using Model Routing to Reduce AI CostsMatthew Berman · Forward FutureA short, sponsored explanation of splitting planning and coding between models of different cost, with a customer savings claim from DigitalOcean.
0:24ENA Surprise in AI Token Usage and CostsMatthew Berman · Forward FutureMatthew Berman reacts to a rising usage bill and notes why monitoring token consumption matters.
0:52ENGPT-5.3-Codex-Spark Brings Faster Coding InferenceMatthew Berman · Forward FutureMatthew Berman highlights OpenAI's smaller coding model and its use of Cerebras hardware for low-latency responses.
0:24ENMatthew Berman Checks His Clawdbot Token CostsMatthew Berman · Forward FutureA short usage check reveals unexpectedly high model spending over two days.
12:13ENOpenAI’s Cerebras Deal and the Race for Faster AI InferenceMatthew Berman, Andrew Feldman, Greg Brockman · Forward FutureMatthew Berman explains why inference speed and computing capacity matter, while a closer look at the agreement and benchmark puts his 100-times-faster scenario in context.
1:33ENOpenAI’s Cerebras Deal and the Limits of a 100× Speed ClaimMatthew Berman, Andrew Feldman · Forward FutureMatthew Berman discusses OpenAI’s 2026 agreement for Cerebras inference capacity and a speed chart. The contract and comparison require more context than the video’s headline provides.
1:38ENNVIDIA’s Groq Licensing Deal and the Inference MarketMatthew Berman · Forward FutureMatthew Berman discusses why NVIDIA licensed Groq’s inference technology and hired members of its team.
11:34ENNvidia’s Groq Licensing Deal: AI Inference, Talent and Competitive StrategyMatthew Berman, Jonathan Ross · Forward FutureGroq investor Matthew Berman examines a non-exclusive technology license and the move of senior Groq staff to Nvidia, and speculates about the future of AI chips.
1:02ENInside a Cerebras Data Center in Oklahoma CityMatthew Berman · Forward FutureMatthew Berman tours the facility and explains its wafer-scale chips, racks and cooling system.
14:07ENInside Cerebras’s Oklahoma City AI Data CenterMatthew Berman, Andrew Feldman, Billy Wooten · Forward FutureMatthew Berman tours the facility with Cerebras CEO Andrew Feldman and Scale Data Centers COO Billy Wooten, exploring wafer-scale chips, liquid cooling and backup power. Their performance and expansion figures are presented as company claims from 2025.
8:47ENWhy Identical LLM Prompts Can Produce Different Answers at Temperature ZeroMatthew Berman · Forward FutureMatthew Berman explains Thinking Machines Lab’s work on batch-invariant inference and what its reproducibility experiment actually showed.
12:10ENMercury Coder and Diffusion Language Models: Speed, Demos and LimitsMatthew Berman · Forward FutureMatthew Berman explores Inception’s 2025 coding model, comparing iterative text refinement with autoregressive generation and examining speed claims.
10:01ENDeepSeek R1 Code Optimization and a $3 Visual Counting ExperimentMatthew Berman · Forward FutureMatthew Berman examines a human-guided coding improvement and a small reinforcement-learning experiment, then considers their implications for AI research.
10:39ENZamba2-7B: A Hybrid Mamba–Transformer Model Tested on Coding and ReasoningMatthew Berman · Forward FutureMatthew Berman compares Zyphra’s efficiency claims with his own informal tests, including Python games, spatial puzzles and simple word tasks.
9:38ENLiquid Foundation Models: Company Benchmarks and a Hands-On TestMatthew Berman · Forward FutureMatthew Berman reviews Liquid AI’s 2024 model and memory claims, then tries the 40B preview on coding, logic, and instruction-following prompts.
0:54ENOpenAI’s o1 Preview: A First Look at Longer ReasoningMatthew Berman · Forward FutureMatthew Berman introduces the o1-preview and o1-mini models and tries a Python game prompt.
0:44ENNVIDIA Adds Multi-LoRA Serving to Its RTX AI ToolkitMatthew Berman · Forward FutureIn an NVIDIA-partnered video, Matthew Berman describes using multiple adapters with one base model and a vendor-reported throughput gain.
5:50ENA Mixture-of-Agents Demo Using Groq and StreamlitMatthew Berman · Forward FutureMatthew Berman walks through a 2024 Groq-powered multi-agent project, from setup to inspecting its layered responses.
11:20ENMatthew Berman Adapts Mixture of Agents to Groq InferenceMatthew Berman · Forward FutureA 2024 tutorial changes an open-source multi-model ensemble to use Groq, then tests its interactive output and fixes a streaming error.
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