--- title: "Dense Slides: WF2026: Software Factories & Keynotes ft. Microsoft, OpenAI, OpenClaw, Z.ai (GLM), MiniMax, HF" category: "slides" video_id: "htM02KMNZnk" sourceLabels: ["Captured video frames", "Local OpenCV slide-region detection"] --- # Dense Slides: WF2026: Software Factories & Keynotes ft. Microsoft, OpenAI, OpenClaw, Z.ai (GLM), MiniMax, HF ## Source Video [WF2026: Software Factories & Keynotes ft. Microsoft, OpenAI, OpenClaw, Z.ai (GLM), MiniMax, HF](https://www.youtube.com/watch?v=htM02KMNZnk) ## Method This deck is slide-only. The existing captured video frame set supplies candidate frames, then local OpenCV rejects sponsor/title/speaker-only frames, crops visible slide surfaces, deduplicates, and saves the cropped slide images. ## Cropped Visible Slides ![[assets/dense-slides/htM02KMNZnk/slide-001.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-001.html) - AI slide classifier: `content_slide` confidence `0.96` - Text source: advanced OCR `rapidocr-live/right-72/contrast`. - OCR decision: ready — Dense multi-level diagram with small labels and many tiny text elements; OCR will be more reliable than manual transcription. Slide text: > Nesled boogrs WNe > Loopcraft: The Art of Stacking Loops > Leel 6 / G > Tokens Turms Tools Tasks Automations > 6 · software factories??????·ar gools, alocate. col tot nee. open eplorwtion > 5.automatom·cron,soogonts,muRr-agem eitcoaorationad cepetisio:adarys > 4 ·paal loop ·run,judga,rety eit:goal reathet 、huy > opent ealt vege off-goa. agent resu wogos me v > 3·agmtturn·caf tooi,tedresun toit: no mare 10ol cafls: ≥ Nn5s > reas_(ite() →240803 rn_tests() > 2·cat lpep·promgt. respond,aign eit Meofuf reney:oe hure > AH·BOAOH > user:“eplainrecursion* asistart: dreft reply humk甲 humpn. elgredrepsy > 1. token loop· sample,appendl repiat tt: atop toetn·secondy > the cat tat ![[assets/dense-slides/htM02KMNZnk/slide-002.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-002.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Small quote blocks and a statistics panel make this OCR-suitable and too dense for manual transcription in triage. Slide text: > # unedited, from the threads HARD DATA: > "different outputs at temperature = 0, "even at temp O you get different: r/LocaiLLaMA answers, you're using GPUs." mostly the MoE architecture." r/LocaiLLaMA 1,000 prompts temp 0. vLLM - Qwen-3-8B - 80 answers > "completely correct or completely wrong,. Hacker News depending on minute numerical diffs." ![[assets/dense-slides/htM02KMNZnk/slide-003.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-003.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Small quote blocks and a statistics panel make this OCR-suitable and too dense for manual transcription in triage. Slide text: > # unedited, from the threads HARD DATA: > : r/locaiLlaMA "different outputs at temperature = 0, "even at temp O you get different answers, you're using GPUs.": r/LocaillaMA. mostly the MoE architecture." 1,000 prompts temp 0: vLLM: Qwen-3-8B - 80 answers > "completely correct or completely wrong.: Hacker News depending on' minute numerical diffs." ![[assets/dense-slides/htM02KMNZnk/slide-004.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-004.html) - AI slide classifier: `content_slide` confidence `0.78` - Text source: none. - Slide text: not surfaced (`none` by AI classifier). ![[assets/dense-slides/htM02KMNZnk/slide-006.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-006.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > It's throughput — how many cycles fit the same window. > 7 cycles > 143 cycles ![[assets/dense-slides/htM02KMNZnk/slide-007.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-007.html) - AI slide classifier: `content_slide` confidence `0.96` - Text source: advanced OCR `rapidocr-live/bright-screen/contrast`. - OCR decision: ready — Mixed stage framing and small bullet text are better handled by OCR. Slide text: > World's Fair AlEngincer > Are these fully-vibed pRs good? > Womantedroiunow > Were real companies, with real codebases, coding this way? > Were the end-to-end Al-generated PRs any good? > Gl Lab Vorld'sFair ALn In what ways did they fail? > Fair enAl groptio > ler. uFair Engineering the future of Al > Fair TADOG ![[assets/dense-slides/htM02KMNZnk/slide-008.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-008.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Small chart labels and callouts are better handled by OCR. Slide text: > AI systems evolved faster than our evaluation methods > The Illusion The Reality > 100% MAA Invisible Failure Hodes > 75% Behavior Degraded Production > 90% 50% 25% Reliability Gaps Unpredictable User > Benchmark Accuracy 0% T-0 T+10ms T+50ms T+10Gms ![[assets/dense-slides/htM02KMNZnk/slide-009.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-009.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: agent_vision. Slide text: > Agenda > 1. Meet OG Assist > 2. The Origin Story > 3. Betting on Effect > 4. The Core Agent Loop > 5. A2A, Evals & Sandboxing > 6. Long Context Handling > 7. Monitoring & Observability > 8. Tools, Skills & Dev Workflows ![[assets/dense-slides/htM02KMNZnk/slide-010.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-010.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > Origin Story > One bet on agents, one immediate yes. ![[assets/dense-slides/htM02KMNZnk/slide-011.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-011.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Dense code and small explanatory text are better handled by OCR. Slide text: > Example inport ( Chat. languagcHodel ) from "@elfect/si" Tho Hamess Our agent loop, > // Streaaing chat with tool suppart leport ( Etfect, Streaa ) fros "cffect" rebuilt > const streanirgchat = Effect.genifunction- () ( > Const chat = yielo- chat.ctoty Effect-native. > Yielce chat > .stresnTexti( > proapt: "Generate a crcative story" > 11 > .pipeiStrean.runforEachiipartl => Effect.synci() = console.toglpart/l!) > The core loop started on LangGraph. It now runs fully > Effect-native typed control flow, structured concurrency, and > Source: https//effect-ts.github "zvt-o0- co- language model if we were to uh the agent loop. resource safety end to end, Aiso allows more granular control t ![[assets/dense-slides/htM02KMNZnk/slide-012.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-012.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: agent_vision. Slide text: > The missing layer between evals and action > Observability > Your observability stack captures every tool call, every LLM completion, and every exception. > Evals > Your eval suite judges whether the final output was correct. > Agent > Context, Skills and .md file > The GAP ![[assets/dense-slides/htM02KMNZnk/slide-013.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-013.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Dense small table/text slide; OCR will be more reliable than manual transcription. Slide text: > In practice, most agent memory frameworks have focused on user continuity: preferences, > profile facts, conversation history, and long-lived personalization. > chat experiences is not self-improving learning system for production agents. 米 > Approsch What It stores Retrleval signal Learns from outcomes? > ta?ut! Tulu iLryCher, hoet Ravr chat hlstory Recency No > Extracted facts, preteroncos Embedding similarity Ho > Tt tttg yiph L'ip'drieia. Entity relationshups over timo Graph travorsal + foconcy No > Verbal self-roflections Simlarity to current task Partialty: reflections capture ranked by outcome lossons, but rotrievat b not > agentRTx Ltlity scores Tast-lrkcd refoctions wth Simitasity weighted by outcome-derived utility Tm tehty tcorat updt sln it ![[assets/dense-slides/htM02KMNZnk/slide-014.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-014.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > Memory as Reasoning > facts > User preferences > Reasoning > "Check settlement before Refund" > Static, > No Context > No history > Reranked based on usefulness > Context is updated based on task > learned from history ![[assets/dense-slides/htM02KMNZnk/slide-015.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-015.html) - AI slide classifier: `title_card` confidence `0.82` - Text source: agent_vision. Slide text: > AI Engineer World's Fair > Engineering the future of AI ![[assets/dense-slides/htM02KMNZnk/slide-016.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-016.html) - AI slide classifier: `content_slide` confidence `0.96` - Text source: agent_vision. Slide text: > Room to act, safely. > Code runs in isolated sandboxes, so agents can take real action without putting production systems or customer data at risk. ![[assets/dense-slides/htM02KMNZnk/slide-017.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-017.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: advanced OCR `rapidocr-live/border-trim/opencv-adaptive`. - OCR decision: ready — Dense table with multiple small cells; OCR is the right extraction path. Slide text: > The Paradigm Shift: Output vs. Behavior > Traditional LLM Evaluation Agent Evaluation > Goal Output Accuracy Workflow Behavior > Environment Static Datasets Dynamic Contexts > Execution Single-path Processing 一 Multi-path & Tool Dependent > Failure Mode Hallucination Cascading Workflow Failure ![[assets/dense-slides/htM02KMNZnk/slide-018.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-018.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > Agents keep failing at the same tasks. > Gartner's 2025 AI deployment survey found that 85% of AI projects fail in production. McKinsey's 2025 State of AI report found that fewer than 20% of AI pilots scale to production within 18 months. ![[assets/dense-slides/htM02KMNZnk/slide-019.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/htM02KMNZnk/slide-019.html) - AI slide classifier: `content_slide` confidence `0.94` - Text source: advanced OCR `rapidocr-live/bright-screen/contrast`. - OCR decision: ready — Dense small code and diagram text; OCR is likely better than manual transcription. Slide text: > World's Fair AlEngineer BEWARE' AI OF POLICE deflim (q: Question) (v: IsProper q): IO (∑ (a: Answer), IsSafeText q v a > World'sFair BEWARE' AI OF. PLAN def Ilm (q: Question) (v: IsProper q): (∑ (a: IO Answer),IsSafelO q v a > Browst. > World's In Code They Act, In Proof We Trust > Erik Meijer / Research Scholar Leibniz Labs ### Hidden Non-Slide Evidence - [`slide-005.jpg`](/assets/dense-slides/htM02KMNZnk/slide-005.jpg) — `title_card` confidence `0.92`; Title card / speaker intro style frame with prominent headshots. Classification audit: `raw/sources/slide-ai-classification/dense/htM02KMNZnk/audit.json`