Date: Sunday, October 11, 2026

Hey {{first_name | AI enthusiast}},

We give you a prompt to test out the new Gemini universal agent and unearth a post on X about a GitHub repo that can help you clone any software!

Of course, we cannot ignore Claude so we also cover how you can make a dashboard using Claude.

Best regards,

— Renjit Philip

PS: If you want to unleash the power of Personal AI agents to grow your business, setup time speak to me, here»

Connect your company’s data platform, such as Amazon Redshift, BigQuery, ClickHouse, Databricks, or Snowflake, and ask a question in plain language. Claude pulls answers from your data, builds the dashboard, and keeps it current as the data changes.

It works with your other connectors too. For example, you can ask for a dashboard of your Salesforce opportunities. Dashboards work well for quick, exploratory questions, like how this week’s signups compare with last month’s. Click any number to see the query behind it, or ask Claude to explain it. Each chart shows when its data was last refreshed.

Dashboards work alongside your BI and analytics tools. When a question needs deeper analysis, send the dashboard from Claude straight to Amplitude, Grafana, Hex, Mixpanel, Omni, Perplexity or PostHog pick up the analysis from there, with Looker, monday.com, and Tableau coming soon.

Use the feature as an analysis interface, not as a substitute for measurement discipline. Ask it to show the query, source table, filters, time window, and last-refresh timestamp. Then reconcile one important number back to the underlying system.

So what? Test one live business dataset and trace every important metric from the dashboard back to its query, source, filters, and refresh time.

Read the source

OpenAI’s release notes say GPT-6.1 Sol now has an Ultrafast service tier in the Responses API. The stated purpose is to reduce the time between generated output tokens. It is available to API users subject to rate limits, with global processing and US and EU data residency.

This is a production decision, not simply a model-quality decision. Lower latency may improve user experience or make an agent feel more responsive, but it may also change cost, throughput, rate-limit behavior, and the economics of retries.

Benchmark the workflow that matters to you. Compare Standard, Fast, and Ultrafast on completion time, useful output, error rate, token cost, and downstream human review. A faster answer is valuable only if it remains good enough for the job.

So what? Benchmark latency, quality, error rate, and cost on one representative workflow before changing a production default.

Read the source

Social Capital’s latest analysis asks whether private credit, rather than AI itself, is where the next concentration risk may appear. It points to a $35 billion vehicle led by Apollo and Blackstone to buy chips and lease them to Anthropic, and to forecasts of very large infrastructure spending by hyperscalers and SpaceX.

The analysis argues that private loans to AI-related companies have carried only slightly higher spreads than other private-credit loans, despite much higher expectations in public equity markets. It also highlights mismatches between debt terms and the shorter lives of customer contracts or leases in some financings.

This is an investment thesis, not a settled diagnosis. Its useful question is who ultimately carries the downside if AI revenue arrives late, financing costs rise, or software borrowers lose customers faster than expected.

So what? When assessing AI infrastructure exposure, map the debt, leases, counterparties, contract duration, and refinancing risk instead of looking only at capex headlines.

Read the source

Google Could CEO announced the Gemini agent and stated: “…Gemini is your new single, universal agent for work. It has all of your business context and can be used for everything from knowledge work to answering questions, and from content creation to coding, all from a single prompt box. It plans the work, uses skills and tools, connects to your systems, and brings back something finished — inside the documents, the inbox, and the developer environments you already work in. It chooses the best model for the job, has built-in cost controls, and most importantly, has the security, administration, and governance required by your company.”

For a prosumer, the useful starting point is not “run my business.” It is one repeatable job that currently costs you 30–60 minutes.

Start with this prompt: “Review the files in this folder. Extract the objective, audience, constraints, open questions, and requested deliverables. Use only these files. Quote the source file for every important claim. Draft the outputs, but stop and ask for approval before sending, sharing, scheduling, buying, or contacting anyone.”

Google says the agent can work across Gmail, Drive, Docs, Slides, Sheets, Chat, and Calendar, and that enterprise deployments can add identity, permissions, sandboxing, audit trails, and spend caps. Those controls may depend on the plan and administrator setup; confirm what is actually enabled in your account.

Use a small folder and a low-risk task first. Measure time saved, corrections required, citations that were wrong or missing, and any action you had to undo.

1.

Developer morluto's REA repository offers a simple MCP server and CLI tool that links AI agents to reverse-engineering powerhouses like Ghidra and IDA Pro. It analyzes native binaries, Electron apps, APKs, and more to explain features with evidence, confidence scores, and recreation steps—all processed on the user's device via a quick npx rea-agents setup command. Examples include dissecting Notion's clipboard handling or a 1996 game's logic, though human checks are still key. Reactions highlight a shift where software secrets matter less, with Naval sharing Amjad Masad's view that AI tools are making all code effectively open-source.

2.

“Token prices keep falling and GPU rental prices keep rising. Intelligence gets cheaper, usage goes up, and a model ends up in every piece of software.”

80% of product professionals say AI helps them ship faster, but customers aren’t seeing value any sooner. Atlassian’s State of Product 2027 explores the gains AI is delivering and the challenges that remain, from decision-making that hasn’t kept pace to gut instinct overriding customer evidence.

Read the report