CEO @ZivergeTech, CEO @GolemCloud, Exec Producer & Creator @TheUltCoder, OSS contributor @zioscala, speaker, writer. Accelerating human dominion. ✝️
Maryland, USA
Joined November 2008
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You THINK your SaaS is safe because customers are locked in. 🙄
But agents don't click your buttons or brand, and can rip your data out one shovelful at a time.
Your GUI is a dinosaur, and the meteor is already here. ☄️
Read how to thrive anyway. 👇
John A De Goes retweeted
Effect v4 is here.
One ecosystem. Zero dependencies.
The next chapter of Effect and the foundation for building reliable software and AI agents in TypeScript.
John A De Goes retweeted
Introducing Caliban Gateway, a new module to combine remote Apollo Federation subgraphs, plain GraphQL services and local Caliban APIs into a single graph.
This has been in the works for more than 3 years, and I am glad to finally release a first version in Caliban 3.2.0. As usual, UX should be quite nice and performance is very competitive 🚀
Give it a try and let me know what you think 🙏
More details here: ghostdogpr.github.io/caliban…
John A De Goes retweeted
Jev decides. Golem remembers. 🧠
New on the blog by @dvigovszky: a log incident pipeline where Jev classifies every log line and routes matches to per-category durable agents, which summarize each incident with an LLM.
No database. Just in-memory state that survives crashes and redeploys.
➡️ golem.cloud/blog/making-deci…
Golem 1.6 is coming out, and the release represents a major advancement for building custom agents, harnesses, and agentic workflows on Golem.
I'll be talking about and showing many features of 1.6 throughout the coming weeks, but more instructive for now is simply the one unifying theme that ties together all of 1.6.
We all know that the most complex type of agent you can build is a coding agent. Coding agents build and run software, and therefore, in theory they can do anything that a software engineer can do--which is pretty much anything.
However, I have argued that all agents should be coding agents, because coding gives agents the ability to make custom software to solve problems they are tasked with--even ordinary business problems like, "Show me top performing accounts in a bar chart and send the top performer a congratulations email."
If all agents must be coding agents, then ultimately, then I reasoned that Golem should be sufficiently powerful to support this use case directly. As a result, with feedback from early users of Golem, I shaped the 1.6 feature set around developing a coding agent that runs entirely in Golem--durably, with a precise permission model, and the ability to add ironclad guardrails to increase reliability.
The results you will see for yourself soon enough, and I believe Golem will have attained the milestone where Golem beats LangChain and friends in any head-to-head comparison.
As proof, we will be shipping a demo coding agent that writes TypeScript, and which runs entirely in Golem, benefiting from Golem's durability and permission model.
Unlike all the other coding agents in this category, this coding agent will run entirely in WASM, not a VM, and feature millisecond startup time, consume MB instead of GB, and attain economies impossible with VM technology.
We're about to enter an age where even the most powerful type of agent--a general-purpose coding agent--runs entirely in its own sandbox, durably, starting in milliseconds, and consuming hardly any memory, enabling heretofore unimagined use cases for general-purpose agents.
Stay tuned for more.
John A De Goes retweeted
Cooked up some really tasty treats over the past 3 months:
- Various Jev morsels; most importantly a Scala ZIO Jev client with a full agentic loop orchestrator: github.com/jamesward/zio-typ…
- Site for testing CIMD: cimd.now
- Support for Mantle in ZIO Bedrock: github.com/jamesward/zio-bed…
- Updates to zio-http-mcp for 2026-07-28 MCP spec: github.com/jamesward/zio-htt…
- ZIO Evals lib: github.com/jamesward/zio-eva…
- ZIO Git lib: github.com/jamesward/zio-git
- sbt over MCP & symbol tools: github.com/jamesward/sbt-mcp
- sbt-tdepver for easier transitive dep mgmt: github.com/jamesward/sbt-tde…
- New Scala & Scala Native buildpacks: github.com/jamesward/buildpa…
github.com/jamesward/buildpa…
Still cooking! A few more new projects in the past 2 months:
- ZIO HTTP MCP client/server library: github.com/jamesward/zio-htt…
- ZIO Bedrock library: github.com/jamesward/zio-bed…
- sbt revolver alternative for sbt 2.0: github.com/jamesward/sbt-rel…
- sbt webjars plugin: github.com/webjars/sbt-webja…
- sbt plugin for sass: github.com/jamesward/sbt-sas…
John A De Goes retweeted
Well, I cooked something
"Meet Zazr: a Vavr fork bringing modern functional programming to Java"
zazr.dev/blog/meet-zazr/?v=2…
John A De Goes retweeted
Replying to @jdegoes @zivergetech
I see massive demand for this service.
If you have a small business, see inefficiency, but don’t know how to solve it, getverger.ai is here to help
My company @zivergetech is launching a small business automation solution:
getverger.ai/
Most of my followers are in tech and won't need it, but you all know folks who could benefit.
Please share it within your network!
What does the rapid success of Jev say about Agent Frameworks??
Jev is a fast and cheap classifier: a type of model that outputs NOT text, but a single number, which shows not only massive cost and latency advantages, but also accuracy advantages on the subset of tasks that it can be used for (bye-bye auto-regression!).
The success of Jev indicates that historical and popular 'agent frameworks' that rely on YAML or JSON objects to specify certain parameters, ranging from model choice to role prompt and tools, are increasingly obsolete.
The future brings with it a diversity of specialized models that excel at specific parts of agentic work. A diversity of specialized models implies specialization in the architecture of agents, which requires flexibility in the 'agent loop'--the harness that leverages models to drive them toward success.
There is no way to encode agents as YAML or JSON properties. A custom agent or harness must necessarily be written in a general-purpose programming language, which has boundless capacity for specialization and customization at every layer.
Every good agent framework is going to evolve to be code-first; the rest will be gone within a year.
John A De Goes retweeted
Home from the best @lambda_conf ever. Thanks to all the cool folks who attended. You were all great.
In the past, if you made something powerful, but unfamiliar, it would die for a simple reason:
Humans hate learning and aren't particularly good at it, and won't pay for migration.
AI is changing this, which could completely rewrite the way we think about viability.
AI is first and foremost an equalizer: a tool of the common man to do uncommon things.
Elites pushing for 'AI regulation' are acting in their own personal self-interest (like they always do).
Non-elites pushing for AI regulation are acting against their own self-interest.
John A De Goes retweeted
ZIO DynamoDB 3.x snapshots are out 🚀
vs Scanamo (props to @aplokhotnyuk):
• ~4× read throughput
• ~4× lower allocation
• ~20% higher write throughput
• ~10% lower allocation
Zero-dependency core. Effect-neutral execution.
github.com/zio/zio-dynamodb/…
#Scala #DynamoDB #AWS #ZIO
John A De Goes retweeted
If you actually believe that major AI dev enterprises are afraid of their own toys, and demanding that the US government regulate them, then please dial the number listed below.
... And we will connect you to a representative from the Federal Witless Protection Program.
This is Silicon Valley we are talking about, the reigning all-time champions of hubris and lack of perspective. Any one of these guys would burn the world if it meant he could be king of the ashes that remained.
No, they want a regulatory moat. A bunch of federal bureaucracy and compliance requirements that they can cut through with money, which will hopelessly entangle the open-source models following close behind them.
Why? And more importantly, why now?
Because AI is not on the verge of superintelligence at all.
Quite the opposite.
It's hit a point of diminishing returns. LLMs are great with language now, but they lack theory of mind, executive function, and a world-object model, and those are not little glue-on doodads. They are problems as deep as language processing, which humanity hasn't even scratched the surface of yet.
Several more fundamental paradigm-shifting breakthroughs will have to occur before humans even build sapience, much less superintelligence.
And AI companies cannot pull these innovations out of their collective asses, because it is the very nature of innovation to show up unexpectedly and by accident.
So why is this a crisis for OpenAI, et al, and why does it put them in danger from open source source models?
It's because of what I call Second Implementer Effect.
It's closely related to Second System Effect.
Ever notice how the first-to-market company doesn't always win in the long run? Ever notice how a second company frequently comes along and eats their lunch?
To develop something entirely new requires a lot of resources. A lot of investment. A lot of engineers.
And that means that once you actually build it, you are a big company with a lot of engineers. And they need something to do. You can't just trim sails and fire them, because you have a lot of investors and need a lot of revenue to pay them.
Line must go up.
So you build lots and lots of new features, for which you can charge more money.
You become Oracle. But 99% of the market doesn't want Oracle.
Customers don't want infinite features for infinite money. They only want more up to a certain point. After that, they want those features, and only those features, for as little money as possible.
And what often happens is that your first implementer shoots right past that point, gold-plating the product until nobody wants to pay for that much gold.
Then the second company, which just offers a basic product at a basic price, eats their lunch. Because the second implementer is the right size for the market.
In this case, the risk to OpenAI, et al, is that open source models will be that second implementer. Nobody is gonna pay giant data centers by the GPU cycle if they can have "good enough" running on their home machine.
And if AI have hit diminishing returns, or soon will (yes), then they can't widen or maintain the gap by speeding up.
They have to slow everyone else down.
You can bet they are burning up the phone lines to their pet senators.
That's why everyone is suddenly telling you the singularity is tomorrow. Precisely because it's not.
The Navier-Stokes thing is a double nothing burger, two juicy helpings of nothing between two slices of nothing, with no cheese, no lettuce, and no tomato.
Have you noticed that no one is telling you about all the wonderful new technologies that will result from the corner case they brute-forced?
Because there aren't any. Its a mathematician's toy, and if you want to know more about how and why, read what Feynman wrote about Banach–Tarski, which will give you the general idea.
Does this mean AI is useless?
Far from it. Large Language Models have already proven useful, for things like research, personalized instruction, coding and so forth. And we've only begun to scratch the surface of what we can do by communicating commands to a computer in natural language.
Cool things will continue to come out of this. But they will happen at the interface and application layers. No superintelligence will be involved.
For the immediate future, AI will remain the paintbrush, not the artist.
And, yes, there are also risks. AI can pretend to be human, and persuade retards of all sorts of things. But that's a human failing. It's not the AI's fault we let the retards vote.
That's how we got a whole bunch of midwit senators who want to "regulate" innovation, instead of medicare fraud, invading third world scum, and invading third world scum who commit medicare fraud.
More goodies coming from the @zivergetech workshop on @EffectTS_ that's happening TOMORROW. 👀
Going to have laminated guides ready to hand out at Effect Days 2026. 🚀
You'll know when AI gets de novo reasoning ('AGI'), because it won't look anything like 10,000 monkeys exploring 991,235 dead ends before finding the correct path.
Given sufficient compute resources, I can now prove or disprove any mathematical conjecture using only a random number generator.
(Yes, this is subtweeting the latest AI hyperbole.)
Many of the last holdouts to AI coding have ceded.
For better or worse--and the truth is, it's both--the industry now understands AI coding is here to stay.
Looking forward to discuss the future of our industry at the last private @lambda_conf.