Not every successful AI product needs to be revolutionary. Some interesting opportunities could be: Automated document processing. Industry-specific reporting tools. Intelligent customer support workflows. AI-powered research assistants. Internal knowledge management. Business data analysis. I'd rather build something that saves a company 10 hours every week than something impressive that nobody actually uses. What industry do you think has the biggest opportunity for practical AI applications?

For three years, managed AI APIs were priced below what heavy usage actually costs to serve. Pilots were cheap, agents were cheap, and nobody asked what a few hundred of them would cost in production. Now flat-rate plans are giving way to metered, token-based billing, and teams running agentic workloads are reporting bills two or three times higher than before. ServiceNow has reportedly burned through its full-year Anthropic budget in the first months of 2026. In this week’s newsletter, we look at what’s really driving the token bleed, and it’s often not the model. When an agent has to dig through a chaotic, ungoverned data lake to find a basic answer, every lookup becomes an expensive round trip. Swapping one LLM for another won’t fix that. Fixing the data foundation and the pipeline around it will. Here are 3 levers that bring AI spend back under control: - 🧮 Token budgets and semantic caching: Learn how to cap what an agent can “spend” on a task directly in the orchestration layer. You’ll also see why an expensive answer about last quarter’s revenue should be generated once, cached, and served locally instead of paid for again with every new question. - 🔓 Open-weight models, with honest math: See how close open-weight models like DeepSeek V4 Pro, GLM-5.2 and Kimi K3 have come to the proprietary frontier, and how to calculate real TCO for self-hosting. With H200 rentals around $4.30 to $4.90 per GPU-hour and frontier-scale models needing far more than a single card, utilization and cluster size decide whether self-hosting saves money or burns it. - 🔀 Hybrid fallback routing: Discover why treating every prompt the same is a recipe for financial ruin, and how policy-driven routing sends queries to a local or hosted open-weight model first, escalating to a premium API only when the request truly needs it. Before you cut your model or your budget, ask which one is actually the problem. A cheaper model lowers the price per token. A clean data foundation lowers the number of tokens. Only one of those keeps working when prices rise again.

Greetings data folks, I hope you are all well. I'm Faaris, a Data Analyst now transitioning to AI Automation Specialist. Have a had a few years of work as a data analyst, mainly experienced in business intelligence, making web data analytical saas, and made Power BI Dashboards for gov and private clients. I love to be part of a community of like-minded people, this community looks to be it for data professionals. You all may know if you have worked in data roles, how ALOT has changed with AI in the playing field (it really levelled the playing field, we can call ourselves senior software devs now haha), so alot of methodologies in data works has changed. One of the things I have been working on is my website that I launched a few days ago, DashPrompts (dashprompts.com), it simply converts your prompt and spreadsheet data into a Power BI Dashboard fast. Thought of making a tool that I'd love to have had a while back to automate the slow development of my PBI dashboards with AI 📊 Here's a clip of my speaking about DashPrompts, are you guys working on similar solutions or using AI in your data work lifecycle in an interesting way? Lets pick eachother's brains 🔢

Every AI agent pitch comes with a promise. Cloud vendors promise elastic scale and frontier models. Terminal-based “local” agents promise your sensitive work never leaves the building. Then someone looks at the network traffic and finds that the “local” agent has been shipping your schemas, code and query results to a cloud-hosted model the whole time. Meanwhile, the cloud agent everyone was afraid of turns out to be easier to lock down than the developer laptop it was supposed to replace. In this week’s newsletter, we investigate what each runtime actually exposes, and why the debate itself misses the point. Most of what gets said about cloud and local agents is either overstated or outright wrong. Where your agent runs matters far less than what sits underneath it: the data foundation it reasons over, the identity it acts under, and whether you can trace every tool call it makes. Here are 3 things the marketing won’t tell you: - ☁️ What really leaves the building in the cloud: Learn why the tools a cloud agent calls usually run inside your own environment, and why the real exposure is the context you send out. You’ll also see how least-privilege agent identities, private connectivity like AWS PrivateLink or GCP Private Service Connect, and MCP gateways with allowlists and kill switches keep that flow contained. - 💻 Why “local” isn’t airtight: Discover why many terminal agents run their harness on your machine but send their context to a cloud model. Even fully local models don’t make the attack surface disappear. It moves to shell access, prompt injection hidden in files, and credentials sitting on developer workstations. - 🧭 Per-workload routing decisions: See why the answer isn’t picking a side. It’s deciding, workload by workload, which data may cross which boundary, under which identity, and with what evidence left behind. It also points to two resources that go deeper on the foundations underneath: Ignition’s article on why your data architecture is holding your AI back, and the Scalefree expert sessions on getting your data platform ready for agentic AI and orchestrating agentic workflows.

Every MCP integration works in the demo. The trouble starts the day an agent reads something it shouldn’t trust. A resume arrives with a hidden instruction buried in the text, the agent dutifully reaches for its database tool, and the only thing standing between your salary table and an external endpoint is a long-lived API key with admin rights that nobody remembers issuing. In this week’s newsletter, we cover the half of MCP adoption the launch posts skip. The Model Context Protocol killed the N × M connector nightmare, but its authorization framework is optional, and nothing in the protocol forces you to switch it on, scope it tightly or put a policy check in front of a tool. That gap is not a model failure. It is an architecture one, and it is exactly where agentic pilots quietly turn into incident reports. Here are 3 guardrails that close it: - 🔑 Delegated OAuth, done right: Learn why static keys should be retired on remote MCP servers, and why “just forward the user’s token” is the one shortcut the spec explicitly forbids. You’ll also see how token exchange gives every downstream call its own user-scoped credential. - ⚖️ A policy-as-code gateway: Discover how a Policy Decision Point running AWS Cedar or OPA checks every JSON-RPC request against hard constraints before a tool ever executes. It also explains why Cedar’s formally verified semantics and microsecond evaluation make that check effectively free. - 🚧 Egress, approvals and audit: See why identity alone won’t stop a prompt injection, and how locking down outbound tools, gating high-impact actions behind a human, and logging every tool call as a privileged event turn a breach into a blocked request. It closes with a practical checklist for data and security engineers. It also points to two resources that go deeper on the foundations underneath: the Scalefree expert session on getting your data platform ready for agentic AI, and Ignition’s article on why your AI is only as trustworthy as your data.