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269개정렬
14:23ENTesting DBRX: Coding Strengths and Reasoning GapsMatthew Berman · Forward FutureMatthew Berman examines Databricks’ open-weight model and puts it through coding, arithmetic and reasoning prompts.
27:32ENFrom a side project to 700 integrations: Harrison Chase on what still blocks agents진행자 Sarah Guo, Elad GilThe LangChain CEO walks through a two-year churn of patterns, and why memory, planning, and few-shot data still matter more than swapping models.
1:28:37ENTwelve Hours After a GPT-4 Demo, He Turned a Decade-Old Legal Startup Around진행자 Nathan LabenzPablo Arredondo, co-founder of CaseText and now VP of CoCounsel at Thomson Reuters, traces legal research from 19th-century citators to a retrieval-anchored GPT-4 product that law firms treat more like a colleague than a search box.
21:16ENArtPrompt: What a 2024 Study Found About Visual Text and Model SafetyMatthew Berman · Forward FutureMatthew Berman reviews an ASCII-art recognition benchmark and reports mixed results from his own informal tests.
23:00ENTesting Google Gemma 7B at Launch: Setup MattersMatthew Berman · Forward FutureMatthew Berman compares local and hosted tests of Google’s early open-weight model.
1:06:39ENOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need Years진행자 Jacob EffronLogan Kilpatrick on picking GPT-3.5 versus GPT-4, why plugins stalled, and why the public internet is not ready for autonomous agents.
19:37ENHow Rephrased Test Data Inflated a 13B Model’s Benchmark ScoresMatthew Berman · Forward FutureMatthew Berman examines a 2023 experiment showing how reworded benchmark questions can evade simple contamination checks and inflate model scores.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%진행자 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.
18:29ENOpenAI's 2023 Leadership Crisis, Q* Speculation and Synthetic DataMatthew Berman, Andrew Ross Sorkin, Elon Musk · Forward FutureMatthew Berman connects Sam Altman's return, an Elon Musk interview and Nathan Lambert's discussion of synthetic training data, separating reported events from his own theories.
32:48ENFourteen People, $200 Million of Compute, and the Reliability Bottleneck for Agents진행자 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.
1:23:10ENLuxury Purses Under $500 Beat a Bank's Fraud Model진행자 Nathan LabenzArthur.ai CEO Adam Wenchel on boundary attacks, LLM firewalls, and why a model that eats raw text cannot be secured from the inside.
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.
55:08ENOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2진행자 Nathan LabenzAt Google I/O in May 2023, PaLM 2 arrived already running in more than two dozen products — and its lead product manager walks through what that actually took.
39:45ENPalantir's 13th Employee: The Model Is a Stochastic Genie. Ontology Is the Stake That Tethers It진행자 Sarah Guo, Elad GilCTO Shyam Sankar on 550 microservices, air-gapped submarines, and why Palantir's ontology is what large language models were waiting for.
5:34ENFreeWilly2: A 2023 Prompt Test with Uncertain FormattingMatthew Berman · Forward FutureMatthew Berman retries an instruction-tuned Llama 2 model after a poor first run, finding mixed results and a likely template problem.
4:59ENTesting Airoboros L2 70B on Coding, Writing and ReasoningMatthew Berman · Forward FutureMatthew Berman runs a small set of prompts against a Llama 2 fine-tune, finding useful structured output alongside failures on code and logic.
11:07ENInstalling Llama 2 13B Chat and Testing Its ResponsesMatthew Berman · Forward FutureMatthew Berman demonstrates a 2023 local setup and tests a 13-billion-parameter chat model on coding, writing, arithmetic and reasoning. His small set of prompts illustrates both useful outputs and mistakes.
9:46ENOrca Mini V2 vs. OpenOrca: A 2023 Informal Model ComparisonMatthew Berman · Forward FutureMatthew Berman tests two Orca-inspired models on coding, writing and reasoning, with results shaped by prompt setup and subjective scoring.
8:04ENWizardLM 13B v1.1 Tested on Coding, Writing and LogicMatthew Berman · Forward FutureMatthew Berman tries an instruction-tuned model on a small set of prompts, with mixed results and several subjective scoring choices.
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.
54:00ENDouwe Kiela on Data, Retrieval and Enterprise AI게스트 Douwe Kiela · 진행자 Harry StebbingsThe Contextual AI CEO discusses language model trade-offs, evaluation, open models and the hurdles to enterprise use.
18:08ENSix Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.
7:21ENTesting Orca Mini 13B, an Unofficial Model Inspired by OrcaMatthew Berman · Forward FutureMatthew Berman loads a quantized Orca Mini 13B and tries coding, reasoning, arithmetic, and summarization prompts. His 2023 informal test produces several failures and a few successes.
13:50ENMPT-30B: Running MosaicML’s Model Locally and Testing Its Chat VariantMatthew Berman · Forward FutureMatthew Berman reviews MosaicML’s June 2023 release, demonstrates a local setup, and puts the hosted chat model through an informal prompt rubric.
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