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Live Agents: GEPA Prompts + Managed Vars",{"provider":7,"model":8,"input_tokens":3708,"output_tokens":3709,"processing_time_ms":3710,"cost_usd":3711},8380,2516,37110,0.0029115,{"type":14,"value":3713,"toc":3964},[3714,3718,3721,3728,3741,3748,3763,3770,3781,3785,3788,3791,3810,3813,3816,3819,3826,3830,3833,3874,3881,3888,3891,3902,3905,3908,3912,3915,3926,3929,3933,3962],[17,3715,3717],{"id":3716},"build-golden-datasets-and-custom-evals-for-reliable-agent-testing","Build Golden Datasets and Custom Evals for Reliable Agent Testing",[22,3719,3720],{},"Samuel Colvin demonstrates optimizing agents post-deployment by first establishing a baseline with structured evaluations against a \"golden dataset\"—manually verified ground truth data. For the case study, he scrapes Wikipedia pages for UK MPs, extracts text via BeautifulSoup, and defines Pydantic schemas for MP details and political relations (focusing on ancestors like parents\u002Fgrandparents, excluding spouses\u002Fchildren).",[22,3722,3723,3724,3727],{},"The golden dataset (",[50,3725,3726],{},"golden_relations.json",") contains exact relations for ~650 MPs, created by running a high-end model like Opus once and manual checks. Custom evaluators compare agent outputs to this truth:",[3729,3730,3731,3738],"ul",{},[3732,3733,3734,3737],"li",{},[39,3735,3736],{},"Accuracy",": Exact match on relations list (1.0 if perfect, partial scores like 0.9 for minor name\u002Fdescription diffs).",[3732,3739,3740],{},"Assertions for relation types, roles, and ancestor filtering.",[22,3742,3743,3744,3747],{},"Key principle: Prefer deterministic, rule-based evals over \"LLM-as-judge\" to avoid bias. \"Defining your own ",[53,3745,3746],{},"evaluators"," is far better than LLM as a judge because the LLM as a judge is effectively the kind of lunatics running the asylum.\"",[22,3749,3750,3751,3754,3755,3758,3759,3762],{},"To run: Load dataset with ",[50,3752,3753],{},"load_dataset()",", register evaluators, then ",[50,3756,3757],{},"dataset.evaluate(agent_func, name=\"eval-name\")"," using Pydantic AI's ",[50,3760,3761],{},"override"," for prompts\u002Fmodels. Concurrency limits (e.g., max=5) prevent rate limits. Results appear in Logfire UI: spans show inputs\u002Foutputs\u002Fcosts, evals tab aggregates metrics (e.g., 85% accuracy for simple prompt).",[22,3764,3765,3766,3769],{},"Common mistake: Over-relying on console logs—disable terminal output (",[50,3767,3768],{},"LOGFIRE_NO_CONSOLE=true",") for clean traces. Before: Simple one-liner prompt gets 85% accuracy, confuses non-ancestors\u002Fpolitical vs. public figures. After better prompt: Improves to ~90%+ by explicitly discounting same-gen relations.",[22,3771,3772,3773,3776,3777,3780],{},"Setup prerequisites: ",[50,3774,3775],{},"uv sync",", Logfire project (",[50,3778,3779],{},"logfire project use demo","), API keys (Pydantic Gateway for multi-model access or direct OpenAI\u002FAnthropic). Quality criteria: High accuracy on ancestors, low false positives on spouses\u002Fkids.",[17,3782,3784],{"id":3783},"evolve-prompts-genetically-with-gepa-on-production-traces","Evolve Prompts Genetically with GEPA on Production Traces",[22,3786,3787],{},"GEPA (Genetic Evolutionary Prompt Algorithm, via \"Jepper\" library) optimizes prompts as strings or JSON by breeding top performers. It evaluates candidates on a dataset, selects Pareto frontier (best trade-offs), mutates\u002Fcrosses them (e.g., mix phrases from high-scorers), and iterates.",[22,3789,3790],{},"Process:",[3792,3793,3794,3797,3800,3807],"ol",{},[3732,3795,3796],{},"Define initial prompts (simple vs. advanced) and models as Pydantic models.",[3732,3798,3799],{},"Run evals on split dataset (e.g., 65 test cases for speed).",[3732,3801,3802,3803,3806],{},"Launch GEPA: ",[50,3804,3805],{},"gepa.optimize(evaluate_fn, initial_candidates, generations=10, population_size=20)",". It parallelizes evals, instruments via Logfire for traces.",[3732,3808,3809],{},"Output: Ranked prompts by composite score (accuracy + cost\u002Fefficiency).",[22,3811,3812],{},"In demo: Simple prompt → 85% acc; advanced (ancestor rules) → better; GEPA evolves hybrids exceeding both (e.g., 92%+ acc). Handles systemic errors like over-including spouses by evolving phrasing: \"Only ancestors (parents, grandparents)—exclude spouses, children, siblings.\"",[22,3814,3815],{},"Trade-offs: Compute-heavy (hundreds of evals\u002Fgeneration); start small dataset. Mistake: Random mutation—GEPA biases toward elites like horse breeding. \"It takes the best racehorses and breeds them... you take all of the best resources and breed them.\"",[22,3817,3818],{},"Extend to production: Use real traces\u002Ffeedback as eval inputs. Future: Autonomous optimization from Logfire.",[22,3820,3821,3822,3825],{},"Quote: \"GEPA is ultimately an optimization library ",[53,3823,3824],{},"that"," optimizes a string... it can be a simple text prompt or some JSON data.\"",[17,3827,3829],{"id":3828},"enable-zero-downtime-tuning-with-managed-variables-in-production","Enable Zero-Downtime Tuning with Managed Variables in Production",[22,3831,3832],{},"Logfire's managed variables let you update any Pydantic-serializable object (prompts, models, params) live without restarts. Define as Pydantic model:",[43,3834,3836],{"className":45,"code":3835,"language":47,"meta":48,"style":48},"from logfire.managed import managed_variable\n\nclass AgentConfig(BaseModel):\n    model: str = \"gateway:gpt-4o-mini\"\n    instructions: str = \"...\"\n\nconfig = managed_variable(AgentConfig)\n",[50,3837,3838,3843,3848,3853,3858,3863,3868],{"__ignoreMap":48},[53,3839,3840],{"class":55,"line":56},[53,3841,3842],{},"from logfire.managed import managed_variable\n",[53,3844,3845],{"class":55,"line":62},[53,3846,3847],{"emptyLinePlaceholder":102},"\n",[53,3849,3850],{"class":55,"line":68},[53,3851,3852],{},"class AgentConfig(BaseModel):\n",[53,3854,3855],{"class":55,"line":74},[53,3856,3857],{},"    model: str = \"gateway:gpt-4o-mini\"\n",[53,3859,3860],{"class":55,"line":80},[53,3861,3862],{},"    instructions: str = \"...\"\n",[53,3864,3866],{"class":55,"line":3865},6,[53,3867,3847],{"emptyLinePlaceholder":102},[53,3869,3871],{"class":55,"line":3870},7,[53,3872,3873],{},"config = managed_variable(AgentConfig)\n",[22,3875,3876,3877,3880],{},"In agent: ",[50,3878,3879],{},"agent = Agent(..., instructions=config.instructions, model=config.model)",". Changes in Logfire UI propagate instantly (poll every 30s).",[22,3882,3883,3884,3887],{},"Production demo: FastAPI server with ",[50,3885,3886],{},"\u002Fanalyze"," endpoint runs agent on live Wikipedia HTML. Update prompt\u002Fmodel via Logfire—tune for better ancestor detection without deploy.",[22,3889,3890],{},"Implicit feedback: Log user thumbs-up\u002Fdown, aggregate into evals. Q&A insights:",[3729,3892,3893,3896,3899],{},[3732,3894,3895],{},"Prompt bloat: GEPA prunes inefficient phrasing.",[3732,3897,3898],{},"Context engineering: Chain-of-thought in prompts.",[3732,3900,3901],{},"Internal use: Pydantic team tunes agents on traces.",[22,3903,3904],{},"Trade-offs: Polling overhead (low); free tier generous. Mistake: Mutable globals—managed vars are safe, versioned.",[22,3906,3907],{},"Quote: \"Managed variables... don't have to be just text they can be effectively any object that you can define with a Pydantic model.\"",[17,3909,3911],{"id":3910},"from-manual-to-continuous-optimization-workflow","From Manual to Continuous Optimization Workflow",[22,3913,3914],{},"Full loop: Golden evals → GEPA on traces → Managed vars deploy → Feedback evals. Fits mid-workshop: Assumes Python\u002FPydantic familiarity, agent-building basics. Broader: Any structured output task (invoices, addresses) benefits.",[22,3916,3917,3918,3921,3922,3925],{},"Exercise: Fork repo (",[50,3919,3920],{},"github.com\u002Fpydantic\u002Ftalks\u002F2024-ai-engineer","), run ",[50,3923,3924],{},"uv run main.py eval --split test --prompt initial",", compare prompts, GEPA optimize, deploy to FastAPI.",[22,3927,3928],{},"Quote: \"Deploying an agent is only the start... change prompts, models... without redeploying.\"",[17,3930,3932],{"id":3931},"key-takeaways","Key Takeaways",[3729,3934,3935,3938,3941,3944,3947,3950,3953,3956,3959],{},[3732,3936,3937],{},"Create golden datasets from high-model runs + manual verification for deterministic evals—beats LLM judges.",[3732,3939,3940],{},"Use GEPA to breed prompts: Start with 2-5 candidates, 10 generations on 65-case split for quick wins.",[3732,3942,3943],{},"Define managed variables as Pydantic models for instant prod updates—no restarts needed.",[3732,3945,3946],{},"Instrument everything with Logfire: Traces reveal confusions (e.g., spouses as ancestors).",[3732,3948,3949],{},"Prioritize ancestor filtering in political\u002Frelation extraction: Evolve phrasing like \"exclude same-gen or descendants.\"",[3732,3951,3952],{},"Run evals in parallel (max_concurrency=5) to optimize costs during optimization.",[3732,3954,3955],{},"For FastAPI agents: Override configs live, log implicit feedback for GEPA inputs.",[3732,3957,3958],{},"Avoid hype: \"I don't really believe in AI observability I think it's a feature not a category.\"",[3732,3960,3961],{},"Scale: Free Logfire tier handles workshops; Gateway simplifies multi-model testing.",[88,3963,90],{},{"title":48,"searchDepth":62,"depth":62,"links":3965},[3966,3967,3968,3969,3970],{"id":3716,"depth":62,"text":3717},{"id":3783,"depth":62,"text":3784},{"id":3828,"depth":62,"text":3829},{"id":3910,"depth":62,"text":3911},{"id":3931,"depth":62,"text":3932},[],{"content_references":3973,"triage":3982},[3974,3978],{"type":3975,"title":3976,"context":3977},"podcast","The Rest is Politics","mentioned",{"type":3979,"title":3980,"context":3981},"other","Jepper (GEPA)","recommended",{"relevance":80,"novelty":74,"quality":74,"actionability":80,"composite":3983,"reasoning":3984},4.55,"Category: AI & LLMs. The article provides a detailed approach to optimizing AI agents using specific techniques like golden datasets and custom evaluations, addressing a key pain point for developers looking to improve production AI features. It includes actionable steps and code snippets that developers can implement directly.","\u002Fsummaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary","2026-05-07 17:00:06","2026-05-08 11:03:29",{"title":3706,"description":48},{"loc":3985},"263bbb77349e4ef1","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A48uhxfxbsM","summaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary",[3995,113,47,114],"agents","Tune production agents without redeploys using Logfire's managed variables for prompts\u002Fmodels and GEPA's genetic algorithm to evolve better prompts from evals on golden datasets.","Hands-on workshop by Pydantic's Samuel Colvin: codes along optimizing an agent for extracting political relations from Wikipedia pages using Logfire evals, GEPA prompt evolution on a golden dataset, and managed variables for live prompt\u002Fmodel tweaks in a FastAPI app—no redeploys needed.",[],"0cZ4pNDJvZh7SpRsu_CCMCCCQN6tzKI9oKJA0ArueRc",{"id":4001,"title":4002,"ai":4003,"body":4008,"categories":4044,"created_at":98,"date_modified":98,"description":48,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":4045,"navigation":102,"path":4053,"published_at":4054,"question":98,"scraped_at":4055,"seo":4056,"sitemap":4057,"source_id":4058,"source_name":4059,"source_type":109,"source_url":4060,"stem":4061,"tags":4062,"thumbnail_url":98,"tldr":4064,"tweet":98,"unknown_tags":4065,"__hash__":4066},"summaries\u002Fsummaries\u002F3534febd058cee34-gemma-4-31b-delivers-frontier-reasoning-on-a100s-w-summary.md","Gemma 4 31B Delivers Frontier Reasoning on A100s with Rigorous Setup",{"provider":7,"model":8,"input_tokens":4004,"output_tokens":4005,"processing_time_ms":4006,"cost_usd":4007},6099,1452,18749,0.00192315,{"type":14,"value":4009,"toc":4038},[4010,4014,4017,4021,4024,4028,4031,4035],[17,4011,4013],{"id":4012},"hardware-demands-set-the-deployment-floor","Hardware Demands Set the Deployment Floor",[22,4015,4016],{},"Gemma 4 31B in 4-bit quantization requires 17–20 GB VRAM to load, ruling out free Colab T4 (16 GB max) and mandating A100-SXM4-80GB (79.25 GB usable) or equivalents like RTX 3090\u002F4090 (24 GB) for inference; QLoRA fine-tuning needs 22–25 GB. Use Unsloth library first for PyTorch\u002FTransformers optimizations, loading via FastModel.from_pretrained(load_in_4bit=True, device_map=\"auto\") then FastModel.for_inference() to cut memory and speed up attention. Fallbacks like Xformers (when Flash Attention 2 fails) maintain functionality without major slowdowns, proving robust workflows tolerate imperfect installs.",[17,4018,4020],{"id":4019},"tokenizer-precision-fixes-silent-inference-bugs","Tokenizer Precision Fixes Silent Inference Bugs",[22,4022,4023],{},"Apply_chat_template() without return_dict=True omits attention mask, triggering pad\u002FEOS token warnings and risking unreliable generation—fix by unpacking **inputs from the dict into model.generate(). This yields consistent, accurate outputs at temperature=1.0, like three witty explanations of ocean salinity via mineral leaching, river transport, and evaporation concentration (e.g., \"giant salt shaker\" to \"over-seasoned soup\"). Correct setup ensures chain-of-thought via internal \u003C|channel>thought\u003C|channel> tags, preserving scientific accuracy and creativity across runs.",[17,4025,4027],{"id":4026},"structured-prompts-unlock-agentic-and-multimodal-depth","Structured Prompts Unlock Agentic and Multimodal Depth",[22,4029,4030],{},"Role-assign system prompts (e.g., \"high-stakes safety diagnostic agent\") with mandated formats (Analysis, Risk Assessment, Mitigation), low temperature=0.4, and max_new_tokens=1024 produce precise aviation diagnostics: pitot-static drift analysis covers q = P_total − P_static, soft vs. hard failures, RVSM noncompliance, stall\u002Foverspeed chains, and autopilot confusion—matching safety literature without hallucination. Multimodal extends to vision: prepend \u003C|image|> tokens from URL-fetched photos (e.g., Golden Gate Bridge yields 200+ tokens), placing images before text queries for native encoder handling, generating structural\u002Fenvironmental reports that leverage visual context transparently.",[17,4032,4034],{"id":4033},"trade-offs-utility-for-rigorous-builders-only","Trade-offs: Utility for Rigorous Builders Only",[22,4036,4037],{},"Open-weight access democratizes frontier capabilities, but A100 cloud costs enforce a hardware floor—opt for smaller E4B\u002FE2B variants on budgets. Prompt architecture shapes cognition: roles\u002Fformat dictate agentic discipline over raw generation. Engineering trumps hype: silent tokenizer errors are riskier than crashes at scale, yet correct patterns yield domain-expert outputs (e.g., ADC windows, RVSM) in seconds, proving Gemma 4 31B's production readiness for reasoning\u002Fvision tasks when hardware and code align.",{"title":48,"searchDepth":62,"depth":62,"links":4039},[4040,4041,4042,4043],{"id":4012,"depth":62,"text":4013},{"id":4019,"depth":62,"text":4020},{"id":4026,"depth":62,"text":4027},{"id":4033,"depth":62,"text":4034},[],{"content_references":4046,"triage":4050},[4047],{"type":3979,"title":4048,"url":4049,"context":3981},"GEMMA4_DEMO.ipynb","https:\u002F\u002Fgithub.com\u002Ffrank-morales2020\u002FMLxDL\u002Fblob\u002Fmain\u002FGEMMA4_DEMO.ipynb",{"relevance":74,"novelty":68,"quality":74,"actionability":74,"composite":4051,"reasoning":4052},3.8,"Category: AI & LLMs. The article provides detailed insights into deploying the Gemma 4 31B model, addressing specific hardware requirements and prompt engineering techniques that are crucial for practical implementation. It offers actionable guidance on optimizing model performance, which aligns well with the needs of developers looking to integrate AI into their products.","\u002Fsummaries\u002F3534febd058cee34-gemma-4-31b-delivers-frontier-reasoning-on-a100s-w-summary","2026-04-20 23:49:50","2026-04-21 15:26:16",{"title":4002,"description":48},{"loc":4053},"3534febd058cee34","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Frunning-gemma-4-31b-in-practice-a-technical-essay-on-capabilities-constraints-and-results-6a9343f599c3?source=rss----f37ab7d4e76b---4","summaries\u002F3534febd058cee34-gemma-4-31b-delivers-frontier-reasoning-on-a100s-w-summary",[4063,113,114,47],"llm","Gemma 4 31B handles witty text gen, agentic aviation analysis, and vision diagnostics on A100 GPUs using Unsloth, but demands 17-20GB VRAM, exact tokenizer flags like return_dict=True, and structured prompts to unlock capabilities without errors.",[],"dCUhhALbU0cwkf43bO8Xp51SIsuEDlfdIvk8e6DOvUI",{"id":4068,"title":4069,"ai":4070,"body":4075,"categories":4101,"created_at":98,"date_modified":98,"description":48,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":4102,"navigation":102,"path":4103,"published_at":104,"question":98,"scraped_at":98,"seo":4104,"sitemap":4105,"source_id":4106,"source_name":108,"source_type":109,"source_url":110,"stem":4107,"tags":4108,"thumbnail_url":98,"tldr":4109,"tweet":98,"unknown_tags":4110,"__hash__":4111},"summaries\u002Fsummaries\u002Fmaster-job-relevant-python-ai-libraries-for-2026-h-summary.md","Master Job-Relevant Python AI Libraries for 2026 Hires",{"provider":7,"model":8,"input_tokens":4071,"output_tokens":4072,"processing_time_ms":4073,"cost_usd":4074},3647,887,8684,0.0011504,{"type":14,"value":4076,"toc":4097},[4077,4081,4084,4088,4091],[17,4078,4080],{"id":4079},"shift-from-generalists-to-production-specialists","Shift from Generalists to Production Specialists",[22,4082,4083],{},"Python boasts 1.19 million LinkedIn job listings, but roles target engineers mastering specific libraries for real systems, not broad knowledge. Beginners fail interviews by learning demo-focused tools that don't scale to production codebases or job descriptions. Success requires picking libraries tied to target AI fields, as the landscape evolves rapidly without clear guidance.",[17,4085,4087],{"id":4086},"evaluate-libraries-by-field-and-impact","Evaluate Libraries by Field and Impact",[22,4089,4090],{},"Prioritize the 5 libraries repeatedly appearing in production and hiring: each serves distinct AI domains (details cut off in source). Use this framework to select: match tool to your career path, verify job relevance via listings, and build deep systems—not superficial demos—to signal hireability. This guide kickstarts targeted learning over scattered exploration.",[22,4092,4093],{},[4094,4095,4096],"em",{},"Content is introductory and truncated before listing libraries, limiting specifics; core lesson is tool-job alignment for employability.",{"title":48,"searchDepth":62,"depth":62,"links":4098},[4099,4100],{"id":4079,"depth":62,"text":4080},{"id":4086,"depth":62,"text":4087},[126],{},"\u002Fsummaries\u002Fmaster-job-relevant-python-ai-libraries-for-2026-h-summary",{"title":4069,"description":48},{"loc":4103},"61e00e4895cc707e","summaries\u002Fmaster-job-relevant-python-ai-libraries-for-2026-h-summary",[47,114],"AI interviews fail on non-production tools; employers seek deep expertise in 5 specific Python libraries amid 1.19M job listings demanding real-system builders.",[],"0LgeuClXQ8J58L2XMZhdZL5qUlFwRqEnwm5plw1-sMg",{"id":4113,"title":4114,"ai":4115,"body":4120,"categories":4154,"created_at":98,"date_modified":98,"description":48,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":4155,"navigation":102,"path":4187,"published_at":4188,"question":98,"scraped_at":4189,"seo":4190,"sitemap":4191,"source_id":4192,"source_name":4193,"source_type":109,"source_url":4194,"stem":4195,"tags":4196,"thumbnail_url":98,"tldr":4197,"tweet":98,"unknown_tags":4198,"__hash__":4199},"summaries\u002Fsummaries\u002Fd2e82aaa08bb6c55-flow-veo-3-tool-for-consistent-cinematic-video-summary.md","Flow: Veo 3 Tool for Consistent Cinematic Video",{"provider":7,"model":8,"input_tokens":4116,"output_tokens":4117,"processing_time_ms":4118,"cost_usd":4119},6051,2389,14060,0.00189785,{"type":14,"value":4121,"toc":4149},[4122,4126,4129,4132,4136,4139,4142,4146],[17,4123,4125],{"id":4124},"consistent-asset-reuse-drives-scene-cohesion","Consistent Asset Reuse Drives Scene Cohesion",[22,4127,4128],{},"Flow generates video 'ingredients' like characters or objects via Imagen text-to-image or user uploads, then reuses them across clips for visual consistency—key for maintaining story continuity without manual tracking. Start with a scene image to spawn new shots, or reference assets in natural language prompts powered by Gemini for intuitive control. Veo 3 excels here with strong prompt adherence, realistic physics, and cinematic quality, letting you iterate effortlessly from idea to polished output. This cuts production time on repetitive elements, enabling focus on narrative over asset recreation.",[22,4130,4131],{},"Trade-off: Early stage means outputs shine in controlled prompts but may need refinement for complex multi-shot sequences.",[17,4133,4135],{"id":4134},"pro-controls-unlock-precise-storytelling","Pro Controls Unlock Precise Storytelling",[22,4137,4138],{},"Camera Controls let you dictate motion, angles, and perspectives directly, mimicking director tools for shots like pans or zooms. Scenebuilder extends existing footage seamlessly—reveal more action or transition to next beats with persistent motion and characters. Asset Management organizes prompts and ingredients for quick reuse. Flow TV showcases Veo-generated clips with exact prompts, so you learn techniques by forking styles (e.g., adapt a dramatic angle from a sample). These features evolve from VideoFX, prioritizing pros while onboarding beginners via everyday language.",[22,4140,4141],{},"Outcome: Professionals ship riskier ideas faster; newcomers prototype without gear costs.",[17,4143,4145],{"id":4144},"subscriber-access-and-proven-filmmaker-outputs","Subscriber Access and Proven Filmmaker Outputs",[22,4147,4148],{},"Available now to U.S. Google AI Pro subscribers (100 generations\u002Fmonth, core features) and Ultra (higher limits, Veo 3 with native audio for sounds\u002Fdialogue). Collaborations validate real use: Dave Clark's 'Freelancers' blends AI with traditional tools for brotherly quests; Henry Daubrez's 'Electric Pink' extends his Veo 2 'Kitsune' (lonely souls tale); Junie Lau's 'Dear Stranger' explores multiverse love. Watch 'Behind the Lens' for their workflows. Early access shaped Flow for creative integration, positioning it as an enabler for diverse voices over replacement.",{"title":48,"searchDepth":62,"depth":62,"links":4150},[4151,4152,4153],{"id":4124,"depth":62,"text":4125},{"id":4134,"depth":62,"text":4135},{"id":4144,"depth":62,"text":4145},[126],{"content_references":4156,"triage":4184},[4157,4161,4164,4168,4171,4173,4177,4179,4182],{"type":4158,"title":4159,"url":4160,"context":3981},"tool","Flow","http:\u002F\u002Fflow.google\u002F",{"type":4158,"title":4162,"url":4163,"context":3981},"Flow TV","http:\u002F\u002Flabs.google\u002Fflow\u002Ftv",{"type":3979,"title":4165,"author":4166,"url":4167,"context":3977},"Battalion","Dave Clark","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5NZubOOeeV0",{"type":3979,"title":4169,"author":4166,"url":4170,"context":3977},"NinjaPunk","https:\u002F\u002Fyoutu.be\u002FbhmZflwma64?si=XiXK-OIL-M2-n_6x",{"type":3979,"title":4172,"author":4166,"context":3977},"Freelancers",{"type":3979,"title":4174,"author":4175,"url":4176,"context":3977},"Kitsune","Henry Daubrez","https:\u002F\u002Fvimeo.com\u002F1047370252",{"type":3979,"title":4178,"author":4175,"context":3977},"Electric Pink",{"type":3979,"title":4180,"author":4181,"context":3977},"Dear Stranger","Junie Lau",{"type":3979,"title":4183,"context":3981},"Behind the Lens: AI, Creativity, and the Future of Filmmaking Tools",{"relevance":68,"novelty":68,"quality":74,"actionability":68,"composite":4185,"reasoning":4186},3.25,"Category: AI & LLMs. The article discusses the Flow tool for video production, which aligns with AI tools and prompt engineering. It provides insights into how the tool can streamline filmmaking workflows, but lacks specific actionable steps for implementation.","\u002Fsummaries\u002Fd2e82aaa08bb6c55-flow-veo-3-tool-for-consistent-cinematic-video-summary","2025-05-20 00:00:00","2026-04-15 15:30:49",{"title":4114,"description":48},{"loc":4187},"d2e82aaa08bb6c55","__oneoff__","https:\u002F\u002Fblog.google\u002Ftechnology\u002Fai\u002Fgoogle-flow-veo-ai-filmmaking-tool\u002F","summaries\u002Fd2e82aaa08bb6c55-flow-veo-3-tool-for-consistent-cinematic-video-summary",[114,113],"Flow uses Veo for prompt-based video clips with consistent characters and scenes, plus camera controls and extensions to streamline filmmaking workflows.",[],"Ias48cTOfrjxymEXsZchFOp8Zl2M5RdHP-AvSjP2Vnc"]