Issue #141: TechInsights Daily — Reasoning Budgets: When Extra Thinking Tokens Pay Off and When They Only Add Latency
sent to 37 subscribers
Issue #140: A Fallback Path Is a Product Decision, Not an Error Handler
sent to 36 subscribers
Issue #139: TechInsights Weekly — Anthropic's Agent Push, OpenAI's Cloud Hat Trick, and Wall Street's AI Jitters
sent to 34 subscribers
Issue #138: TechInsights Daily — Anthropic's Agent Push, OpenAI's Cloud Hat Trick, and Wall Street's AI Jitters
sent to 33 subscribers
Issue #137: Keep a Not-Yet List: The Model Failures You Decided Not to Fix
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Issue #136: TechInsights Daily — AI Finds a Decades-Old Web Flaw — While Attackers Weaponize Agents to Exploit the Next One
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Issue #135: Your Eval Set Has a Shelf Life
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Issue #134: TechInsights Daily — Temperature Zero Is Not Determinism: What You Can and Can't Reproduce in LLM Calls
sent to 25 subscribers
Issue #133: Ask Before You Answer: Clarifying Questions Are a Design Decision
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Issue #132: TechInsights Daily — Parallel Tool Calls Need a Failure Policy: What to Do When Three of Five Calls Succeed
sent to 27 subscribers
Issue #131: Delete One Instruction From Your Prompt Every Quarter and See What Breaks
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Issue #130: TechInsights Daily — Chunk Retrieval Documents by Structure First, Then Tune the Size With a Measurement
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Issue #129: A Glossary in Your Prompt Is Cheaper Than a Fine-Tune
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Issue #128: TechInsights Daily — Write Tool Errors for the Model: Error Messages an Agent Can Actually Recover From
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Issue #127: Write the Postmortem for the Near Miss
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Issue #126: TechInsights Daily — Agent Memory Needs a Retention Policy: Deciding What to Persist, What to Expire, and What to Never Store
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Issue #125: Decide What 'Fast Enough' Means Before You Choose the Model
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Issue #124: TechInsights Weekly — A Pinned Model Will Be Retired: Treat Model Deprecation as a Migration Project
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Issue #123: TechInsights Daily — New model in the catalog: NVIDIA-Nemotron-Labs-Dory1.0-4B-MXFP8
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Issue #122: TechInsights Daily — Crafty OpenAI agent willingly stole crypto from an account it didn't own to complete a task during test
sent to 25 subscribers
Issue #121: TechInsights Daily — A Pinned Model Will Be Retired: Treat Model Deprecation as a Migration Project
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Issue #120: Every Fixed Bug Should Leave an Eval Case Behind
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Issue #119: TechInsights Daily — Amazon’s always-listening Bee wearable can now help you buy stuff
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Issue #118: TechInsights Daily — Schema Design for LLM Output: Field Order, Enums, and Honest Escape Values
sent to 25 subscribers
Issue #117: A Metric Without a Denominator Is a Rumor
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Issue #116: TechInsights Daily — Majority Voting for LLM Answers: When Sampling Several Times Beats Rewriting the Prompt
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Issue #115: Treat Model Output as Untrusted Input to Your Own System
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Issue #114: TechInsights Daily — Send the Model Less: Data Minimization and Reversible Redaction for LLM Prompts
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Issue #113: Pin the Date in Your Prompt Tests, or Your Evals Will Drift on Their Own
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Issue #112: TechInsights Weekly — When Your Prompt Gives Two Instructions That Collide: Write the Precedence Rule
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Issue #111: TechInsights Daily — When Your Prompt Gives Two Instructions That Collide: Write the Precedence Rule
sent to 25 subscribers
Issue #110: Comment Your Prompts: The Line Nobody Remembers Adding Is the Line Nobody Dares Remove
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Issue #109: TechInsights Daily — Evaluating Summaries by Claims, Not Vibes: Separating Faithfulness From Coverage
sent to 25 subscribers
Issue #108: A Score Without an Eval Set Version Is Just a Number
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Issue #107: TechInsights Daily — Versioning Stored LLM Output: Schema Stamps, Upcasters, and Deliberate Backfills
sent to 25 subscribers
Issue #106: Put a Stopping Rule on Prompt Tweaking Before You Start
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Issue #105: TechInsights Daily — 'Not Stated' Is an Answer: Designing Extraction Prompts That Don't Invent Missing Fields
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Issue #104: How Much Model Output Can a Human Reviewer Actually Check?
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Issue #103: TechInsights Daily — Open Weights Are Having a Moment: Mistral's Trillion-Parameter 'Le Chonk' and Reflection AI's Beam Challenge the Closed
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Issue #102: Write Down What the Feature Is Not For
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Issue #101: TechInsights Daily — Etched fields funding offers at $40B+ valuation, sources say
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Issue #100: TechInsights Daily — Anthropic's $2 Trillion Gamble Exposes the Circular Economics of the AI Boom
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Issue #99: Put a Date on Every Claim About What the Model Can't Do
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Issue #98: TechInsights Weekly — Checking Citations Mechanically: Verifying That a RAG Answer's Quotes Really Exist in the Source
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Issue #97: TechInsights Daily — Checking Citations Mechanically: Verifying That a RAG Answer's Quotes Really Exist in the Source
sent to 25 subscribers
Issue #96: Test Your Output Parser Against the Model's Worst Day, Not Its Best
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Issue #95: TechInsights Daily — Measuring Over-Refusal: Testing the Requests Your Model Declines That It Shouldn't
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Issue #94: Name Things Once: Inconsistent Terminology Is a Hidden Prompt Bug
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Issue #93: TechInsights Daily — Dynamic Few-Shot Selection: Choosing Examples Per Request Instead of Per Prompt
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Issue #92: Your Prompt's Examples Are Part of Your Spec, So Review Them Like One
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Issue #91: TechInsights Daily — RPA's Agentic Makeover: HCLTech's Robotiq.ai Deal and UiPath's Cartographer Signal a Market in Transition
sent to 25 subscribers
Issue #90: An Eval That Has Never Failed Might Not Be Measuring Anything
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Issue #89: TechInsights Daily — A Week of Reckoning: OpenAI's Safety Holdback, Anthropic's Existential-Risk IPO, and AMD's Big Bet on 'Physical AI'
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Issue #88: Small Eval Sets Need Error Bars: A 3-Point Gain on 50 Examples Is Probably Noise
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Issue #87: TechInsights Daily — Negative Examples in Prompts: When Showing the Model What Not to Do Backfires
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Issue #86: Run the Cheap, Deterministic Check Before You Ask a Model to Judge
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Issue #85: TechInsights Daily — Dario Amodei of Anthropic to Dine With Trump at White House
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Issue #84: TechInsights Weekly — UiPath's AI Features, Explained: Coding Agents, Autopilot, and the Rest of the FUSION 2026 Lineup
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Issue #83: TechInsights Daily — Google tests buying from Walmart-owned Flipkart through Gemini and AI Mode in India
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Issue #82: TechInsights Daily — UiPath's AI Features, Explained: Coding Agents, Autopilot, and the Rest of the FUSION 2026 Lineup
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Issue #81: TechInsights Notes — The Rollback You're Missing Is the One for Your Prompts
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Issue #80: TechInsights Daily — AI's Price War Heats Up as OpenAI and Anthropic Trade Blows — and Trump Tries to Rename the Technology Itself
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Issue #79: Your AI Feature's README Skips the One Section That Matters Most
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Issue #78: TechInsights Daily — Building a Shared Prompt Library Without It Turning Into Tribal Knowledge
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Issue #77: The Successful Runs You Never Look At Are Hiding Your Next Incident
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Issue #76: TechInsights Daily — AI's Trust Problem Meets Its Trade War: Gemini's Break-Out Hack, the Trump-Xi Summit, and Google's Open-Source Robotics
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Issue #75: Rehearse the AI Off-Switch Before You Need It
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Issue #74: TechInsights Daily — Turning Thumbs-Up/Thumbs-Down Signals Into Better Prompts and Evals, Without Chasing Noise
sent to 25 subscribers
Issue #73: Retrieval Is a Ranking Problem, Not a Search Problem
sent to 25 subscribers
Issue #72: TechInsights Daily — Washington Gets an 'AI Force,' Anthropic Brings in Outside Watchers, and China's StepFun Undercuts the Frontier on Pric
sent to 25 subscribers
Issue #71: The Explainability You Need Isn't "Why Did the Model Think That"
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Issue #70: TechInsights Daily — Inside AI's Reckoning: A Ten-Day Safety Scare Reshapes the Industry's Priorities
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Issue #69: Why Two Engineers Can Grade the Same Model Output Differently — And What That Costs You
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Issue #68: Your Prompt Is Quietly Coupled to the Model You Tuned It On
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Issue #67: Why the First Bug Report About an AI Feature Rarely Names the Real Problem
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Issue #66: The Difference Between Debugging a Deterministic Bug and Debugging a Probabilistic One
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Issue #65: The Onboarding Gap: What a New Engineer Needs to Know About Your AI Feature That No Doc Captures
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Issue #64: The Difference Between a Flaky Eval and a Genuinely Regressed Model
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Issue #63: Why Code Review for a Prompt Change Needs a Different Rubric Than Code Review for Code
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Issue #62: Your Fallback Model Path Is Only as Good as the Last Time You Actually Used It
sent to 22 subscribers
Issue #61: Why a Rollback Plan That Only Covers Code Isn't a Rollback Plan
sent to 22 subscribers
Issue #60: Why an AI Feature Needs Its Own Error Budget, Separate From Uptime
sent to 22 subscribers
Issue #59: Why Your AI Feature Needs a Failure Taxonomy, Not Just an Error Rate
sent to 21 subscribers
Issue #58: Why Passing Your Eval Suite Isn't the Same as Being Ready to Ship
sent to 21 subscribers
Issue #57: Treat Your Tool Schema Like a Public API, Not an Implementation Detail
sent to 21 subscribers
Issue #56: Before You Ship the New Model, Run It in Shadow Mode First
sent to 20 subscribers
Issue #55: Why Every AI Feature Needs a Named Owner, Not Just a Named Model
sent to 14 subscribers
Issue #54: Pre-Register Your Success Metric Before You Ship the Feature
sent to 13 subscribers
Issue #53: QA test issue � email template verification
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Issue #52: The Case for a Designated Skeptic on Every AI Feature Team
sent to 10 subscribers
Issue #51: Closing thought: treating an LLM feature like a distributed system, not a function call
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Issue #50: Why "the model will improve" is not a reason to skip guardrails today
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Issue #49: The tension between flexibility and predictability in agent design
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Issue #48: What a genuinely useful AI feature removes from a user's day, not adds
sent to 3 subscribers
Issue #47: Why teams underestimate the cost of maintaining eval infrastructure
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Issue #46: The case for keeping a human-readable audit trail of agent actions
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Issue #45: Designing prompts around what the model tends to get wrong, not just right
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Issue #44: Why "add more context" has diminishing and sometimes negative returns
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Issue #43: What changes about code review when some of the code was AI-drafted
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Issue #42: The difference between an assistant and an automation, and why it matters for review
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Issue #41: Building an escape hatch: what happens when the model gets it wrong in production
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Issue #40: Why sampling a small number of real failures beats reading every log
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Issue #39: The case for boring, explicit prompts over clever, implicit ones
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Issue #38: What a good incident review looks like for an AI feature
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Issue #37: Designing for the failure case, not just the happy path
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Issue #36: Why "add a system prompt" is not a fix for an unclear product decision
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Issue #35: The maintenance burden nobody budgets for: prompt drift over time
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Issue #34: Reading a model's refusal as information, not an obstacle
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Issue #33: What breaks first when you remove the human from a workflow
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Issue #32: The tradeoff between one big prompt and a pipeline of small ones
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Issue #31: Why review queues are a feature, not a compromise
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Issue #30: Building confidence scores that are actually useful
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Issue #29: The real reason chain-of-thought prompting sometimes backfires
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Issue #28: What "good enough" means differently for a draft tool versus an autonomous one
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Issue #27: Versioning prompts like you version an API
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Issue #26: Why retrieval quality matters more than prompt cleverness
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Issue #25: The case against over-engineering your first agent
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Issue #24: What changes about testing once an LLM is in the loop
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Issue #23: Writing prompts for a team, not just for yourself
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Issue #22: The difference between a hallucination and a stale assumption
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Issue #21: Rate limits are a design constraint, not an edge case
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Issue #20: Why "it worked in my test" is not evidence for production
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Issue #19: What a good fallback strategy looks like when a provider is down
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Issue #18: The quiet cost of prompt sprawl across a codebase
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Issue #17: Debugging a multi-step agent: where to actually look first
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Issue #16: Why human review gates exist even when the model is usually right
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Issue #15: Cost per successful task, not cost per token, is the metric that matters
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Issue #14: The difference between an agent that plans and one that just chains calls
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Issue #13: What changes when an agent can take irreversible actions
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Issue #12: Structured output is a contract, not a suggestion
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Issue #11: Why "the model is smart enough to figure it out" is a red flag in a spec
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Issue #10: Latency budgets: where the milliseconds actually go in an LLM feature
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Issue #9: When a smaller, cheaper model is the right engineering call
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Issue #8: The unglamorous work of writing good eval sets
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Issue #7: Reading model output like a skeptic: what confident-sounding wrong answers look like
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Issue #6: Why tool-use failures are usually a schema problem, not a model problem
sent to 3 subscribers
Issue #5: The case for testing prompts like code, not tuning them like vibes
sent to 3 subscribers
Issue #4: Context windows are not a substitute for good retrieval
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Issue #3: What actually breaks when you scale an AI feature from 10 users to 10,000
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Issue #2: The gap between a demo and a production agent
sent to 3 subscribers
Issue #1: Why "just add more examples" is not a prompt strategy
sent to 3 subscribers