Dmytro Rashko 🇺🇦🇨🇿

Platform Architect @ Amdocs | AI Ops | #KAgent | (He/Him) |

[soup @IRONICHANDLE]

CAN YOU PISS OFF MAN

[antirez @antirez]

I'm so tired of all the AI bots here. Note, however, how at the base of everything wrong there is human stupidity and greed.

[Microsoft AI @MicrosoftAI]

Seven new models launching at Build: let’s go!
Reasoning. Code. Image. Transcribe. Voice.

Built from scratch on a clean data lineage, designed for efficiency, working seamlessly as a family of models

Thread 🧵
#MSBuild

[Nous Research @NousResearch]

The next evolution of Hermes Agent is here!

Introducing Hermes Desktop: everything you love about Hermes, now native on your machine.

First demoed in Jensen's GTC keynote, it's now in public preview.

[elvis @omarsar0]

Very good advice on self-improving agents.

(bookmark it)

This is something I am seeing in my own experiments with coding agents and harnesses for long-horizon tasks.

What I have found is that stronger models do not always evolve better agents.

The current believe in self-evolving agents is that a bigger model writes better prompt and skill edits, so devs put their best model in the evolver seat.

New research shows that intuition is mostly wrong.

The work separates two abilities that usually get conflated. Producing harness updates stays flat across model capability, so Qwen3.5-9B writes edits roughly as good as Claude Opus 4.6. Benefiting from those updates follows an inverted-U that peaks at mid-tier models, while weak models fail to even activate the edits and strong models have little headroom left.

This is important to understand as it tells you where to spend. Put a cheap model on the evolver and your expensive model on the solver, because the gains land solver-side, not evolver-side.

Paper: https://t.co/8kJwR7NhmV

Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

[Andrew Ng @AndrewYNg]

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations.

The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below.

The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs.

However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality.

Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on.

What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities.

[Original text: The Batch newsletter]

[J A Z I I @notjazii]

Chinese AI labs are not slowing down

MiniMax M3 just dropped and it’s looking good:

> 1M context
> native multimodal
> super cheap with 50% launch discount
> strong coding benchmarks
> beats GPT-5.5 and Gemini 3.1 Pro on SWE-Bench Pro

gonna test it this week and share my experience

which model should i compare it with first?

[Jack @jackcoder0]

Two economists just published a mathematical proof that AI will destroy the economy.

Not might. Not could. Will — if nothing changes.

The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled.
The conclusion is one sentence.
"At the limit, firms automate their way to boundless productivity and zero demand."

An economy that produces everything. And sells it to nobody.
Here is how you get there.
A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself.

Because the workers who were fired were also customers.
When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation.

The loop has no natural exit.
The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements.
Every single one failed in the model.
The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger.

No government has implemented this. No major economy is seriously discussing it.
Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion."
Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem.

Rational behavior. At scale. Simultaneously. With no mechanism to stop it.
Two economists built the math. The math leads to one place.

Source: Falk & Tsoukalas · Wharton School + Boston University ·

[Daniel San @dani_avila7]

NVIDIA built exactly what I needed to secure agent skills

https://t.co/y8Lt309tBq

Adding it as a GitHub Action to https://t.co/pSU4AtqPi9

Every community-submitted skill gets scanned before it goes live

No prompt injection, no data exfiltration, no supply chain risks

[clem 🤗 @ClementDelangue]

llama.cpp with MTP support makes local models fast enough to use as daily drivers 🚀

Qwen3.6-27B dense generation below on A10G: From 25 tok/st to 45 tok/s (+78%)!

[Dmytro Rashko 🇺🇦🇨🇿 @dimetron]

@TeksEdge @ArtificialAnlys @Italianclownz MiniMax M2.7 is my favorite model so far and @ollama cloud providing best experience until I will be able to run it at home.

[Steve💙🇨🇦 @xyster]

https://t.co/ZIAF9Uwzz7

This folder documents a fresh Ubuntu 24.04 bring-up performed on 2026-05-23 for:

Lasimeri/MiniMax-M2.7-int4-AutoRound
4x Intel Arc Pro B70 32 GB
vLLM
Context: 2048
prompt 512, output 1536, tensor parallel 4
Mean total/effective throughput: 110.896 tok/s

[David Hendrickson @TeksEdge]

🚨 🤯 @xyster did it! This is insane! Imagine running MiniMax M2.7 locally! One of the best open source models running locally on 4x @Intel B70 ARC Pros w/128GB of VRAM @ 83 tps!

While not cheap, $4K will get you Intel 4 cards while 1 x @nvidia RTX-5090 32GB will cost $5K or RTX-6000 w/96GB costs $10K but neither will run MiniMax M2.7.

[Peter Steinberger 🦞 @steipete]

I'm late to the party, but cmux is great. https://t.co/8uuStvqwcm

current split:
codex mac app: knowledege work, learning, reading
cmux + codex cli: coding

[宝玉 @dotey]

Hermes Agent 架构的文档推荐直接看官方的文档，写的还算清楚。

然后用 Codex 或者 Claude Code 打开项目代码库，直接让 Agent 给你解释代码库，如果不清楚的你随时追问，这样比较好就是你可以问任何你想知道的问题，它会通过检索项目文档和代码，帮你解释的清清楚楚。

官方文档：https://t.co/keVDn8oVDN

[Dan Shipper 📧 @danshipper]

We’ve automated every single thing we can @every with AI agents.

And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3.

I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI.

After Automation: https://t.co/Lb7SUCduAg

[Jaana Dogan ヤナ ドガン @rakyll]

🌟 Today, we are releasing Google’s open source distributed agent runtime.

Agent Executor (AX) is a general purpose runtime and aims to solve dynamic scheduling, resumption, auto recovery, auditing, and trajectory branching from kernel snapshots in agentic workloads.

[GitHub @github]

1/ We are sharing additional details regarding our investigation into unauthorized access to GitHub's internal repositories.

Yesterday we detected and contained a compromise of an employee device involving a poisoned VS Code extension. We removed the malicious extension version, isolated the endpoint, and began incident response immediately.

[Jaana Dogan ヤナ ドガン @rakyll]

Antigravity CLI and SDK are now both available globally! With Gemini 3.5 Flash, Antigravity is my favorite harness. Fast and incredibly intuitive.

[Rohit Ghumare @ghumare64]

https://t.co/xtL6kMjVul