Three major shifts are reshaping AI: value moves from models to harnesses, frontier LLMs become commodities, and intelligent systems evolve into networks of specialized agents. Explore seven data-driven predictions on enterprise AI, open-source models, document workflows, and the future of AI infrastructure.
Category: Code
All things coding and tech
MCP is Dead
Its time to stop trying to fix MCP.
When Everyone Can Build Software, Shipping Stops Being the Advantage
In the age of infinite code, the most valuable person in the room isn’t the one who can build the most; it’s the one who knows what is actually worth building.
The End of Code Scarcity: Why AI’s Real Value is the Power to be Wrong.
How does coding change when coding is cheap, but getting it wrong isn’t.
What I Learned by Developing a Top Alexa Skill.
Top 1% of Skills There are over 160,000 Alexa skills available worldwide. Amazon does not publish rankings or engagement scores, but reviews are visible on the site. Voicebot estimates that less than 1% of skills receive more than 100 ratings. My Skill Tarot Reader is a game / educational tool. Users can have their Alexa explain specific tarot…
No One Wants To Talk To Your Chatbot
Dearly beloved, we are gathered here today to talk about this thing called chat. Electric word, “chatbot”. It means automation of human interactions, and that’s a mighty cost-effective thing. But I am here to tell you, no one wants to talk to your chatbot. So before you call up your programmers, and ask them about ChatGPT….
Comparing Different LLM Models For Data Extraction
A lot of human and computer processing time is spent getting data from one format to another. Many processes defy automation because they run on a long tail of unstructured data, human text, and documents designed for printing. The explosion of AI and large language models (LLMs) opens the door to fully automated data extraction…
Big Data Is Not Enough
Originally posted to LinkedIn on Aug 25, 2014 You Don’t Want to Need Big Data Janitors When Big Data hit the scene, technologist told us to throw away our databases, our spreadsheets, and our models. This was a brave new world where simply having tons of data would answer everything. A 2008, Wired article prognosticated “The End…
My Day At Bletchley Park
Bumph Place I think that I shall never see A sight so curious as BP, This place called up at war’s behest, And peopled by the strangely dressed; Yet what they do they cannot say, Nor ever will ’til Judgement Day. For six long years we have been there, Subject to local scorn and stare. We…
Jira: Epics, Sub-Epics, and Story Points
I love Jira, probably more than I should, but the difficulty in tracking progress and breaking down work to mirror my teams kills me. The new Greenhopper 6.2 and the “edit in place” fields made a big difference, but I still needed a couple of things: A way to groups a bunch of Epics together…
Software Estimation that Works, Part 1: Ranges. Confidence and Risk
Estimates Cannot Be Precise “How long will that take?” No other question stops a tech team dead in it’s tracks. Without exaggeration, it is like asking a new parent, “So how much do you think it will cost to send you kid to college?” If you are that parent and paid your own way, or…
How Software Estimates Fail Part 4: Lots of Little Estimates
Last week we talked about how easily estimates go wrong when you try to make giant ones up front, but they can also fail easily when you try to add up lots of small ones. Once again, it comes down to simple math and how optimistic we are about our planning. If I were to…