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TWiT.tv is a technology podcasting network located in the San Francisco Bay Area with the #1 ranked technology podcast This Week in Tech hosted by Leo Laporte. Every week we produce over 30 hours of content on a variety of programs including Tech News Weekly, MacBreak Weekly, Windows Weekly, Security Now, Intelligent Machines, and more.
Bad reviews shouldn’t be your crash detection system. 🔍😵💫
We added automatic quality alerts for Meta Quest that monitor crash rates, frame rates, startup time, and star ratings after every release. Subscribe once, catch problems before your players do.
That’s just one of seven updates covering pricing, free trial conversion, audience analytics, and more.
🔗 Explore the expanded toolkit at the link in the comments.
AI spending is accelerating faster than most organizations can track it, and the billing data coming from model providers and cloud platforms is not yet granular enough to tell teams where the money is actually going. Without a common framework, engineering and finance teams are flying blind on one of the fastest-growing line items in their budget.
In this exclusive interview with Swapnil Bhartiya at TFiR, Mike Fuller, Member of the Technical Staff, Tokenomics Foundation, breaks down how organizations can gain visibility into AI token spend, connect costs to business outcomes, and build governance before budgets spiral.
Key Topics Covered:
– Why token cost applies to both API-based inference and on-premises model deployments, including hardware procurement, energy, cooling, and lifecycle management
– How the Tokenomics Foundation structures AI value across three domains: production, consumption, and monetization, and where the biggest efficiency opportunities sit today
– The critical gap between cloud billing data and observability telemetry, and why teams must pair both data sets to understand cost at the team, application, and operation level
– Practical first steps for organizations already mid-journey on AI adoption, including scoping internal productivity AI versus customer-facing product AI separately
– Why optimizing token consumption can degrade output quality, and how the industry needs new tooling and best practices to balance cost and performance without compromising results
Welcome to This Week’s Open Source Digest Stay ahead with the latest in open source, technology, and digital trends. From groundbreaking AI advancements to crucial privacy discussions, here’s what you need to know. AI and Industry Innovations Powering the Next Era of AI: Google, Microsoft, and Nvidia are collaborating to standardize and accelerate the transition … Read more
Navigating the AI Wave: From Cost Efficiency to Ethical Dilemmas The open source ecosystem is in a whirlwind of activity, with AI continuing to dominate discussions. From model performance and cost optimization to governance and ethical concerns, the community is grappling with the rapid evolution of AI technologies. Recent videos highlight a dual focus: leveraging … Read more
Model ML is solving the “last-mile problem” in finance, carrying asks from research to analysis to a finished PowerPoint deck or Excel workbook with traceable sources.
When Model ML tested GPT‑5.6 Sol in its Composite, the results “blew every metric out the water,” says Co-founder and CEO Chaz Englander:
⚡ 21% fewer tokens per PowerPoint deck than Fable 5
✅ A 16.6 percentage-point lead over Opus 5 on decks ready for substantive review
📊 36% fewer tokens per Excel workbook than Opus 5
Watch Englander walk through Model ML’s evaluation process, then read the full story about how they use the model in production here: https://openai.com/index/model-ml/
Prompt caching can cut the input cost of long AI agent sessions dramatically—but only when your harness preserves reusable prompt prefixes. This video explains what prompt caching actually stores, why agent costs compound across turns, how provider behavior differs, and the implementation mistakes that invalidate your cache.
—
🔗 *Links*
– Written tutorial: https://alejandro-ao.com/tutorials/prompt-caching/
– Tau coding agent: https://github.com/huggingface/tau
– Pi coding agent: https://github.com/badlogic/pi-mono
– Hugging Face Inference Providers: https://huggingface.co/docs/inference-providers/index
– OpenAI prompt caching: https://platform.openai.com/docs/guides/prompt-caching
– Anthropic prompt caching: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
– Gemini context caching: https://ai.google.dev/gemini-api/docs/caching
—
👋 *Connect with me*
– My website: https://alejandro-ao.com/
– X (Twitter): https://x.com/_alejandroao
– LinkedIn: https://www.linkedin.com/in/alejandro-ao/
—
🤓 *Topics Covered*
– How prompt caching works
– AI agent token cost reduction
– Cache-friendly agent harness design
—
⏱️ *Timestamps*
0:00 Why long AI agent sessions get expensive
0:45 What prompt caching actually caches
5:55 Provider pricing and cache discounts
9:19 Prompt caching best practices
15:40 Summary and cache monitoring
AI policy should be active, not academic. Greig Cowan (Head of AI & Data Science at NatWest) discusses shifting from static checklist reviews to a "Governance as Code" model where compliance checks run continuously at runtime and telemetry is gathered automatically.
🌐 More about FINOS: https://www.finos.org/
📧 Join our newsletter: https://www.finos.org/sign-up
🎙️ Listen to our Open Source in Finance Podcast:
https://www.youtube.com/@FINOS/podcastsLinkedIn: https://www.linkedin.com/company/finosfoundation
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