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Insights on AI, machine learning, and technology strategy
AI Development·
JetBrains Mellum2 shows where agent costs are really going
JetBrains' Mellum2 release is a useful signal for teams building AI workflows: stop treating model choice as one default setting and start routing each step to the smallest model that can pass its receipt.
6 min read
AI Development·
Before agents touch your brand assets, fix the approval layer
AI brand asset management needs more than shared folders. Before agents search, remix, or publish creative assets, teams need approval status, rights, provenance, owners, and workflow receipts.
7 min read
Industry Insights·
ChatGPT memory is a business boundary, not just a convenience feature
OpenAI's new memory work points to a practical question for teams: what should an assistant remember, what should expire, and what should never enter memory at all?
7 min read
Industry Insights·
Do not let an AI support bot reset credentials by itself
AI support bot security gets serious when a chatbot can change email addresses, reset credentials, or move account ownership.
6 min read
Industry Insights·
AI vulnerability triage needs evidence packets, not alert floods
Security teams can use AI to prepare vulnerability evidence, but patch decisions still need deterministic signals, review queues, and audit trails.
7 min read
Industry Insights·
Your next AI monitor needs a feed, not a scrape
If an AI agent monitors competitors, regulations, vendor updates, or research, the feed contract matters as much as the model.
6 min read
Industry Insights·
CAPTCHAs can still spot AI agents. Your browser automation plan needs a fallback.
Browser agents are useful when the task is bounded and the failure path is designed first. Treat third-party verification as a boundary, not a problem the agent will always solve.
5 min read
AI Development·
System prompts are not an agent control plane
AI agents need enforcement points before risky tool calls run. System prompts can guide behavior, but refunds, emails, account deletion, and customer work need runtime policy, approvals, logs, and receipts.
8 min read
AI Development·
The fast-food chatbot problem is a guardrail problem
Viral fast-food chatbot screenshots are funny because the failure is ordinary: the bot is supposed to help with lunch, but the model underneath still wants to be a general assistant.
7 min read
Industry Insights·
Make an agent autonomy map before your AI agents act
Gartner warns that one uniform AI agent governance policy will fail in production. Teams need to map what each agent can observe, advise, approve, or do autonomously before granting access.
7 min read
Industry Insights·
Agent analytics should answer whether the work got done
Customer-facing AI agents need more than traces and token charts. The useful dashboard starts with the job: whether the customer got helped, where the agent hit a wall, and when a human had to step in.
7 min read
Industry Insights·
The CAPTCHA problem is really a browser-agent readiness test
Browser agents can pass a demo and still fail in production when a vendor portal decides the process does not look human. Treat CAPTCHA and bot-detection friction as an operations readiness test before launch.
6 min read
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AI Development
Implementation notes for building AI tools around real business data, handoffs, review queues, and safeguards.
73
Announcements
Product notes, service updates, and BaristaLabs news that affect how small teams use AI at work.
7
Industry Insights
AI market news translated into workflow decisions, risk boundaries, and practical next steps for small businesses.
343
Machine Learning
Model concepts explained through thresholds, queues, and error costs that small teams can actually manage.
10
Small Business AI
Plain-language guidance for owners and operators choosing one useful, reviewable AI workflow at a time.
81
Technical Tutorials
Hands-on guides for approval policies, shadow weeks, agent receipts, and other AI workflow controls.
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