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109 articles
How to Create an Editable AI PowerPoint—and Prove It Is Editable
A PPTX filename does not prove editability. Use this three-slide exercise to verify native text, chart data, separate images, save, and reopen behavior.
Can Claude Watch Videos? A Transcript-and-Frame Workflow
Learn what a transcript can prove, when frames are needed, and how to request a timestamped video summary without pretending Claude received the full video.
How to Build Traceable AI Notes in Obsidian
Use five portable Markdown files to preserve sources, separate judgment from evidence, limit what AI receives, and verify the answer chain.
How to Back Up and Restore Codex Work When Moving Computers
Separate projects, configuration, conversation context, and credentials; then verify file integrity and a real work path in an isolated migration exercise.
AI Technical Weekly | 2026-W36: GPT-6 Astra, Gemini 3.8 Flash, and Agentic Video
GPT-6 Astra, Gemini 3.8 Flash, and agentic video understanding bring distinct questions about runtime control, GA model evaluation, and demand-driven multimodal retrieval.
AI Technical Weekly | 2026-W35: Inference Chips, Hardware Agents, and Multimodal Models
OpenAI Jalapeño, Anthropic MHS, and Gemini Omni 1.1 Flash move inference efficiency, hardware-agent control, and multimodal workflows forward without proving production readiness.
How to Complete Your First Reviewable Task With Codex
Complete your first Codex task with safe source material, a bounded request, and a review checklist that lets you verify every important result.
Should You Add Context, Examples, or a Reference Format?
Diagnose why an AI result missed the mark, then add only the context, examples, or reference format that solves the specific gap.
How to Fix an AI First Draft Without Starting Over
Keep what works, describe the expected result, actual result, and gap, then run one focused revision you can review against the original task.
How to Check Whether AI Citations and Numbers Are Real
Verify that each AI citation exists, locate the exact supporting passage, recalculate important numbers, and check the surrounding context before use.
How Should You Verify AI Facts, Inferences, Advice, and Decisions?
Classify AI output before checking it: trace facts to evidence, test inferences, compare recommendations, and keep final decisions with a named person.
How Do ChatGPT Data Boundaries Change Across Accounts and Apps?
Map the account owner, plan terms, connected apps, administrators, data flow, and retention before using work material in ChatGPT.
How to De-Identify Data or Build Synthetic Practice Material
Identify direct and indirect identifiers, preserve only the task structure you need, and create synthetic examples that do not represent real people.
Should AI Work Live in a Project, Folder, or Conversation?
Organize recurring AI work by duration, shared context, and saved outputs so projects, folders, and conversations each have a clear role.
Which Instructions Belong in AGENTS.md?
Save durable, scoped, testable project rules in AGENTS.md. Keep one-off deliverables, temporary preferences, and stale facts in the current task.
Why Does AI Forget, and How Should You Manage Versions?
Separate temporary context, retrievable memory, and authoritative files so AI work can survive long conversations without silently using stale versions.
What Is a Skill, and Which Work Is Worth Turning Into One?
Use four checks—repetition, stable steps, defined inputs and outputs, and reviewable failure—to decide whether a workflow deserves a reusable Skill.
What Problems Do Plugins, Connectors, and MCP Solve?
Choose an extension by the missing capability: packaged behavior, access to external data, or a protocol that exposes tools and resources.
What Is an AI Agent? Chat, Work, and Agentic Tasks Explained
Compare chat, longer-form AI work, and agents by steps, tool use, external actions, and human control so you can choose the safer entry point.
How to Run a Safe First AI Agent Experiment
Use copied files and four observable stages—read, plan, act, review—to test an AI agent without external logins or irreversible actions.
How to Design Agent Permissions, Approvals, Stops, and Recovery
Limit access first, require approval before consequential actions, define stop conditions, and verify a recovery path before the agent runs.
How to Prove an AI Workflow Is Repeatable Before Automation
Test standard input, variations, missing data, errors, and same-input reruns. Record intervention and decide go or not yet before scheduling.
How to Build a Team SOP From a Personal AI Workflow
Document purpose, roles, inputs, steps, checks, exceptions, and version ownership, then test the SOP with someone who did not design it.
How to Test Whether a Skill, Plugin, or Workflow Is Reliable
Write the expected behavior first, then test normal, boundary, failure, rerun, and permission cases with saved evidence and a clear decision.
How Should Teams Assign Responsibility for AI Work?
Use a simplified RACI matrix to assign data, process, access, approval, incident, and version responsibilities with explicit escalation paths.
How to Design a Small, Measurable, Stoppable AI Pilot
Choose one low-risk use case, a small group, and a short period. Compare quality, time, revision, and failures, then decide go, revise, or stop.
How to Choose Your First AI Task: Start Small, Low-Risk, and Reviewable
Choose a useful first AI task with four checks and a simple task selection card. Start with safe material, a reviewable draft, and no irreversible action.
How Does Generative AI Produce Answers—and Why Can It Be Confidently Wrong?
Learn why generative AI can produce fluent but incorrect answers, then use a simple boundary map to separate drafts, facts, and decisions that need verification.
Chat, ChatGPT Work, or Codex? A Beginner's Guide to Choosing Where to Start
Choose Chat, ChatGPT Work, or Codex based on whether you need a conversation, a reviewable deliverable, or a technical project change.
Codex for Beginners: Understand the Workspace, Conversation, File Changes, and Command Results
Start using Codex with a read-only exercise that helps you distinguish the workspace, conversation, actual file changes, and command results.
Codex Permission Setup: Choosing a Folder, Approvals, and Network Access
Learn what Ask for approval actually allows in Codex, then use a beginner checklist to verify the folder boundary, file edits, and network access.
A Repeatable AI Image Workflow: From Brief to Reviewed Prompt
Turn an AI image request into a repeatable workflow with a clear brief, human review gate, stop conditions, and reusable records—without promising unattended automation.
Can You Put Company Data into AI? Four Checks Before You Upload It
Before you upload company data to AI, check the data, account and service, your authority to share it, and whether a smaller or synthetic alternative can do the job.
Can You Trust an AI Answer? Five Checks Before You Use It
A fluent AI answer is not automatically ready to use. Check facts, calculations, sources, judgments, and recommendations before relying on it at work.
AI Meeting Notes: Turn Source Text into Decisions and Action Items You Can Review
Use AI meeting notes to turn authorized source text into reviewable decisions, action items, owners, and unknowns—without inventing commitments, dates, or responsibility.
How to Write Better AI Prompts: Define the Work Before You Tune the Prompt
Define the goal, usable context, constraints, and a done condition to turn a vague AI request into a first draft you can inspect.
AI Technical Weekly | 2026-W34: Agent Execution, Tools, and Data Boundaries
AWS and OpenAI updates on cross-Region inference, tool-source filters, and ZDR-compatible safety processing make execution, tool, and data boundaries a joint agent-design task.
AI Technical Weekly | 2026-W33: Agent Supply, Tool Protocols, and Task Economics
Gemini 3.7 Flash and DeepSeek V4-Pro reached general availability, making supply, tool-protocol fit, and task scheduling part of agent adoption.
AI Agent Definition of Done: How to Define Completion
Reliable agents need observable completion criteria. Define tasks, outcomes, failure cases, and human calibration before treating an AI workflow as dependable.
AI Agent Evaluation: Why “Done” Is Not Completion
An agent response, an artifact, a verified result, and a real-world outcome are different things. Use a four-layer definition of completion for AI agent evaluation.
What Claude’s Public Engineering Guides Teach About Reliable Agents
A practical guide to Claude and Anthropic's public material on harnesses, multi-agent systems, skills, evaluation, and human control in reliable AI workflows.
When Does Claude’s Multi-Agent Guidance Recommend Splitting Work?
Claude's public multi-agent guidance points to context overload, true parallelism, and independent verification—not a default need for more agents.
How Claude Distinguishes Prompts, Skills, Tools, and Subagents
Claude's public guidance distinguishes a one-time prompt, a reusable skill, tool or MCP access, and a scoped subagent by the job each layer performs.
Human-in-the-Loop AI Agents: Where Humans Should Stay
Human-in-the-loop does not mean approving every action. Use reversibility, permission, and consequence to place people at the decisions that matter.
What Is an Agent Harness? Lessons from Claude’s Public Engineering Guides
Claude's public engineering guides show how an agent harness defines context, tools, stop conditions, permissions, and traceability.
AI Output Is Not Knowledge: Add a Candidate Layer and Human Review
Do not write every AI output into a knowledge base. Use a candidate layer that preserves sources, scope, uncertainty, sensitivity, and human review.
Avoid Agent Sprawl: When to Use a Task, Board, Skill, or Profile
Do not turn every request into an agent. Use five questions to choose a Task, Board, workspace, Skill, or Profile and understand each boundary.
Separate Personal and Work AI Systems with Minimal Information Handoffs
Design separate personal and work AI entry points, share only the constraint that work needs, and do not mistake Profiles for security isolation.
From Dispatcher to Work Chief of Staff in a Multi-Agent System
Before routing ambiguous work, clarify the outcome, identify unknowns, choose the smallest structure, and define an explicit handoff.
AI Weekly Digest, August 10, 2026: AI Coding as a Control-Plane Problem
GitHub's weekly updates show why AI coding teams need explicit controls for reasoning cost, review effort, MCP access, model rollout, and product dependencies.
Building an AI Content Workflow Platform, Part 1: Turning an Ambiguous Idea into a Verifiable MVP
A real project breakdown of how a software PM used evidence, a Lean PRD, non-goals and acceptance conditions to reduce an ambiguous AI idea to a verifiable MVP workflow.
Building an AI Content Workflow Platform, Part 2: Turning Model Integration into a System Specification
A real project breakdown of how a software PM turned model integration into contracts for data lineage, Job lifecycle, readiness, human decisions and private Connector boundaries.
Model Cost, Access, and Open Weights Are the New AI Battleground
AI deployment costs extend beyond model pricing: compare per-task costs, restricted model access, cloud accelerators, and weight transfer within clusters.
AI Agents Are Becoming Systems, Not Features
Long-running AI agents require trajectory monitoring, operating controls, and new infrastructure. See what current evidence proves—and what it does not.
Agent Infrastructure Has Entered the Benchmark Era
AA-AgentPerf uses agent execution traces to compare infrastructure. Understand what parallel-agent capacity and power efficiency can reveal about AI systems.
AI Agent Evaluation Is Becoming a Product Category
Strands Evals, Agent-EvalKit, and Langfuse address different evaluation jobs: diagnosing agent failures, assessing execution paths, and observing behavior.
What AI-Assisted Acceptance Testing Still Cannot See
Website acceptance tests can pass while video playback and visual behavior remain wrong. Three cases show where screenshots and automated checks need follow-up.
Managing an AI Team Instead of Editing the Version Myself
A website redesign becomes a case study in managing AI roles: defining outcomes, inviting disagreement, reviewing results, and preserving context at handoffs.
Why Enterprise AI Adoption Is Slower Than Silicon Valley Expects
An Aaron Levie interview prompts a closer look at enterprise AI adoption: human accountability, agent supervision, and organizational work beyond the model.
Why I Keep AI Tools Isolated During Collaboration
Separate AI conversations, share files, and keep human judgment at the handoff. A collaboration experiment explains why deliberate context isolation matters.
Five AI Agent Patterns—and When Not to Use Them
Compare tool use, planning, multi-agent coordination, and metacognition through Microsoft's agent lessons, including failure modes and when to avoid each pattern.
Browser Use vs. MCP: Choosing the Right Level of Control
Browser control, Playwright, MCP, A2A, and NLWeb solve different connection problems. Compare their roles and where a mixed approach makes sense.
Four Context Failures—and the Cost Paradox Behind Them
Microsoft's context and memory lessons frame four context failures, mitigation choices, and trade-offs among memory tools, information quality, and cost.
Agentic RAG vs. Traditional RAG: Five Differences That Matter
Agentic RAG can rewrite queries, change retrieval methods, and check its own results. Compare that loop with traditional RAG and when the added complexity helps.
Why I Turned Seven Workflows into Skills
Seven custom workflows reveal how to choose between a Skill, CLAUDE.md guidance, and shared rules when organizing repeatable work in Claude Code.
Claude Code Runs an Agent Loop, Not Better Autocomplete
Claude Code cycles through planning, execution, and verification. Learn how that agent loop changes task definition, feedback, and collaboration with the tool.
Context Is Claude Code's Scarce Resource
Treat Claude Code context as a limited budget: use session resets, compaction, and persistent project guidance to keep instructions available and relevant.
Three Ways Claude Connects to Enterprise Work
Connectors, Enterprise Search, and Research give Claude different roles in enterprise work, from accessing tools and internal knowledge to investigating questions.
Understanding Claude as an AI Work Platform
Map Claude's Chat, Cowork, and Code interfaces alongside Projects, Artifacts, and Skills to understand how conversations become organized work.
Design the Knowledge Base, Not the AI Tool
A personal knowledge-system design moves beyond choosing an AI tool. Three reframing questions lead to a four-layer architecture and a more durable foundation.
What Happens After an AI System Passes UAT?
UAT for a market-intelligence pipeline exposes failed cases and informs phased rollout and adoption measures before a team can rely on the system.
The Limits of Zero-Cost Community Monitoring
A cross-language monitoring experiment exposes limits in free community data. Follow the reporting diagnosis and the resulting change in the system's purpose.
Why I Chose APIs Over an Agent Framework
A cross-language intelligence project uses a four-module pipeline and direct Claude API calls. The architecture record explains why an agent framework was unnecessary.
Building Cross-Language Market Intelligence with AI
Turn uneven reports from overseas colleagues into requirements for cross-language market intelligence, with a defined AI role and a deliberately bounded MVP.
Enterprise RAG: From Go/No-Go to Evaluation
A document-Q&A experiment follows RAG from go/no-go through evaluation and optimization, comparing retrieval quality with the scale limits of context stuffing.
Query Rewriting Raised This RAG Score from 77 to 88
A RAG optimization record links retrieval diagnosis to query rewriting and verification. See why some proposed improvements were selected and others deferred.
How to Diagnose RAG Retrieval Quality
Diagnose RAG answers by labeling retrieved chunks and tracing failed questions. The experiment separates retrieval problems from the model's ability to answer.
What 200 Files Revealed About RAG Data Preparation
Controlled document-Q&A experiments compare RAG, keyword search, and context stuffing, showing how information scale changes the appropriate retrieval approach.
Six Decisions to Make Before Building a RAG Pipeline
A RAG build record explains choices in chunking, embeddings, vector storage, and search configuration, including the alternatives and compromises at each step.
Does Your Knowledge Base Really Need RAG?
Before building RAG, test a document knowledge base against simpler baselines. This experiment examines context stuffing and the limits of keyword retrieval.
Four Retrieval Methods to Compare Before Choosing RAG
Compare vector search, BM25, hybrid search, and knowledge graphs by how they retrieve information, the problems they fit, and the trade-offs they introduce.
NVIDIA OpenShell: A Security Architecture for Autonomous Agents
NVIDIA OpenShell separates agent execution from policy enforcement. Examine its sandbox, policy engine, and privacy routing as parts of an agent security design.
Designing a Multi-Agent Content Production System
A content-production design divides writing, source processing, and website maintenance across AI roles, with human decisions, error checks, and an MVP boundary.
What 80,000 People Actually Want from AI
Anthropic's user research shows AI hopes and concerns coexisting. The article examines expectations around productivity and empowerment alongside reliability and risk.
Migrating a Website Architecture with AI in One Day
A WordPress-to-headless website migration illustrates prioritization, rollback planning, staged verification, and the management decisions behind AI-assisted implementation.
NVIDIA Is Selling AI Factories, Not Just GPUs
NVIDIA's GTC announcements connect chips, rack systems, networking, and agent software. The analysis asks what an AI-factory strategy changes beyond GPU sales.
Designing Fact-Checking into an AI Content Pipeline
An AI content pipeline tackles repetitive writing through role boundaries, platform-specific communication, and fact-checking mechanisms built into the process.
Context Engineering in the Million-Token Era
Manage an AI agent's working information through permanent guidance, on-demand loading, session handoffs, and context isolation instead of relying on prompts alone.
A Handoff Pattern for Cross-Session AI Work
A practical HANDOFF.md pattern for carrying decisions, current state, next steps, and known issues across AI coding sessions.
Why Microsoft Built Copilot Cowork on Anthropic
Microsoft's Copilot Cowork announcement puts multi-step enterprise tasks in focus. The article examines Anthropic's role and the shift from drafts to execution.
Why Claude Code Gets Worse in Long Sessions
A Claude Code workflow redesign traces missed instructions and unstable output to overloaded context, then organizes project guidance through a layered architecture.
Why Anthropic Acquired Vercept for Computer Use
Why Anthropic acquired Vercept, what its computer-use team contributes, and why Claude's earlier OSWorld score should not be attributed to the acquisition.
When AI Marketing Has to Move Beyond Traffic
When algorithmic traffic fades, what remains of a brand? This marketing reflection distinguishes rented attention from trust, understood value, and lasting relationships.
How AI Is Reshaping Competition Among Tech Giants
AI competition links data ownership, computing resources, and vertically integrated platforms. Explore the analogy with semiconductor manufacturing and open competition.
AI Platforms, Data Control and the Case for Specialist Providers
An industry viewpoint on how data, content and distribution shape AI platform competition, and where specialist providers may find room to compete.
Taiwan's AI Advantage Needs an Industrial Ecosystem
Taiwan's role in AI extends across technology suppliers and industrial capabilities. The analysis considers how a connected ecosystem can support its competitive position.
TSMC's Global Strategy in the AI Infrastructure Race
A strategic analysis considers TSMC's position amid U.S.–China AI competition, including global expansion, technological leadership, and supply-chain pressures.
The U.S. AI Manhattan Project and Global Competition
A review of the USCC's proposed AI Manhattan Project examines its strategic aims and possible implications for global technology competition and national capability.
How the U.S. CHIPS Act Reshapes Taiwan and China
An analysis of the U.S. CHIPS Act examines manufacturing subsidies, export restrictions, and the opportunities and pressures facing Taiwan and China's chip industries.
Where Quantum Computing and AI Actually Intersect
Quantum computing introduces a different way to process information. Explore its underlying principles, possible connections with AI, and development challenges.
Mapping the AI Server Supply Chain
Follow the AI server supply chain from component design to application deployment, with the roles, constraints, and relationships that connect its participants.
Beyond Silicon: The Materials That Could Extend Moore's Law
As silicon scaling becomes harder, semiconductor development turns to new materials, advanced packaging, and transistor architectures. Review the main directions.
How AI and IoT Are Reshaping Smart Manufacturing
Smart manufacturing combines IoT and AI within production processes. The article connects core technologies with factory applications and implementation challenges.
Why Cerebras Matters to AI Computing
Cerebras uses wafer-scale computing for demanding AI workloads. Examine its chip-design approach and how it differs from NVIDIA's computing architecture.
The Hidden Upstream Layers of Semiconductor Manufacturing
Semiconductor production depends on equipment and materials suppliers. Map the upstream roles of lithography, manufacturing tools, and the firms behind them.
How AI Is Accelerating Superconductor Discovery
Google DeepMind's GNoME illustrates AI-assisted materials exploration. The article discusses graph networks, candidate discovery, and possible superconducting applications.
NPUs and the Shift to Heterogeneous AI Computing
NPUs form part of heterogeneous computing alongside other processors. Learn how specialized hardware fits AI workloads and where the architecture may be useful.
How AI Semiconductors Power Model Training
AI training relies on matrix computation and specialized chips. Compare the roles of GPUs, TPUs, and ASICs, and how training needs differ from inference.
AI Fundamentals: What the Technology Can and Cannot Do
An introduction to AI explains learning, language understanding, judgment, and problem-solving, then relates those capabilities to practical uses and limitations.