ContentPosts from @odirleisias..
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@faun shared a link, 1 month, 4 weeks ago

How we’re responding to The New York Times’ data demands in order to protect user privacy

OpenAI is challenging a court order stemming from The New York Times' copyright lawsuit, which mandates the indefinite retention of user data from ChatGPT and API services. OpenAI contends this requirement violates user privacy commitments and sets a concerning precedent. While the company complies ..

How we’re responding to The New York Times’ data demands in order to protect user privacy
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@faun shared a link, 1 month, 4 weeks ago

The AI 4-Shot Testing Flow

4-Shot Testing Flowfuses AI's lightning-fast knack for spotting issues with the human knack for sniffing out those sneaky, context-heavy bugs. Trim QA time and expenses. While AI tears through broad test execution, human testers sharpen the lens, snagging false positives/negatives before they slip t..

The AI 4-Shot Testing Flow
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@faun shared a link, 1 month, 4 weeks ago

God is hungry for Context: First thoughts on o3 pro

OpenAIjust took an axe too3pricing—down 80%. Entero3-prowith its $20/$80 show. They boast a star-studded 64% win rate against o3. Forget Opus;o3-pronails picking the right tools and reading the room, flipping task-specific LLM apps on their heads...

God is hungry for Context: First thoughts on o3 pro
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@faun shared a link, 1 month, 4 weeks ago

GenAI Meets SLMs: A New Era for Edge Computing

SLMspower up edge computing with speed and privacy finesse. They master real-time decisions and steal the spotlight in cramped settings like telemedicine andsmart cities. On personal devices, they outdoLLMs—trimming the fat with model distillation and quantization. Equipped withONNXandMediaPipe, the..

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@faun shared a link, 1 month, 4 weeks ago

Modern Test Automation with AI(LLM) and Playwright MCP (Model Context Protocol)

GenAI and Playwright MCP are shaking up test automation. Think natural language scripts and real-time adaptability, kicking flaky tests to the curb.But watch your step:security risks lurk, server juggling causes headaches, and dynamic UIs refuse to play nice...

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@faun shared a link, 1 month, 4 weeks ago

The End of Static AI: How Self-Evolving Meta-Agents Will Reshape Work Forever

Meta-agent architectureunleashes AI agents to craft, sharpen, and supercharge other agents—leaving static models in the dust. Amazingly, within a mere 60 seconds, one agent slashes response times by40%and boosts accuracy by23%. The kicker? It keeps learning from real data—no human nudges needed...

The End of Static AI: How Self-Evolving Meta-Agents Will Reshape Work Forever
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@faun shared a link, 1 month, 4 weeks ago

Meta reportedly in talks to invest billions of dollars in Scale AI

Metawants a piece of the$10 billion pieat Scale AI, diving headfirst into the largest private AI funding circus yet.Scale AI'srevenue? Projected to rocket from last year’s $870M to$2 billionthis year, thanks to some beefy partnerships and serious AI model boot camps...

Meta reportedly in talks to invest billions of dollars in Scale AI
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@faun shared a link, 1 month, 4 weeks ago

BenchmarkQED: Automated benchmarking of RAG systems

BenchmarkQEDtakes RAG benchmarking to another level. ImagineLazyGraphRAGsmashing through competition—even when wielding a hefty1M-tokencontext. The only hitch? It occasionally stumbles on direct relevance for local queries. But fear not,AutoQis in its corner, crafting a smorgasbord of synthetic quer..

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@faun shared a link, 1 month, 4 weeks ago

Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks

Tekton plusBuildpacks: your secret weapon for training GPT-2 without Dockerfile headaches. They wrap your code in containers, ensuring both security and performance.Tekton Pipelineslean on Kubernetes tasks to deliver isolation and reproducibility. Together, they transform CI/CD for ML into something..

Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks
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@faun shared a link, 1 month, 4 weeks ago

What execs want to know about multi-agentic systems with AI

Lack of resources kills agent teamwork in Multi-Agent Systems (MAS); clear roles and protocols rule the roost—plus a dash of rigorous testing and good AI behavior.Ignore bias, and your MAS could accidentally nudge e-commerce into the murky waters of socio-economic unfairness. Cue reputation hits and..

What execs want to know about multi-agentic systems with AI