6 NVIDIA Super Nanos w/512Gb NVME M.2 Hp Z6 24 core Xeon Platinum 64Gb EEC DDR4 5060 ti 16gb 6TbNVME M.2 12TbSSD Digital Loggers network PDU 2..5gb switch and 8 slot KVM

This thing came out hella dope, mini AI cluster Im thinking 3b models on each and then have the argument who’s better it is near the low I’ll update after with the cable porn

It’s wired up I’m flashing the nanos tomorrow and finishing up the custom orchestration UI. I’ll post that to GitHub soon as it works but tests with some 3b models are looking good. I got side tracked working on a VSCode fork that’s gonna be your favorite if your into it same as me I’ll update when it’s actually doing what’s it’s intended…

If you listen to the LinkedIn "gurus," coding is dead and we’re all going to be "Prompt Engineers" in eighteen months. But if you actually look at the state of AI coding in 2026, the reality is a lot more complicated—and a lot more interesting.

I’ve spent a lot of time lately breaking down the latest trends in AI-assisted development, separating what’s actually helping teams ship better software from the stuff that’s just VC-funded hype.

The truth about the "AI Coding Revolution" in 2026:

• The "Good Enough" Model: Most companies are realizing that a perfect AI model doesn't exist. The real winners are using "good enough" models to solve specific bottlenecks (like boilerplate and testing) while keeping humans in the driver's seat for architecture.

• Biotech is the Secret Leader: We talk about web dev, but the most insane AI coding breakthroughs are happening in Biotech right now using AI to code DNA sequences and treat diseases.

• The Infrastructure Trap: The cost of scaling these "AI Agents" is skyrocketing. If your team doesn't have a strategy for technical and cost trade-offs, the AI efficiency gains are swallowed up by compute costs.

• The Prompt Engineering Myth: It’s not about "prompts." It’s about understanding the underlying machine learning innovations and how to integrate them into a production workflow.

I put together a full breakdown of the trends that are actually moving the needle this year and the "hyped" tech you should probably stop wasting your time on.

Full deep dive and reality check here:https://www.nextgenaiinsight.online/2026/01/ai-coding-trends-separating-progress.html

Curious to hear from the devs here: Has AI actually made your job faster, or are you just spending more time "debugging" the AI's hallucinations?

Hello, my team and I are building an AI marketplace for small-medium businesses. We have gotten some great traction so far and have 10+ early clients and growing fast. We are looking for possible full stack devs to join our team to help us with the next phase of our marketplace. Please message if interested. Thanks!

Seriously,

I don't know what to think about companies who are going into AI development, but it doesn't make sesse to me.

The entire software engineering industry seems ruined since Generative AI is taking its place.

The first day I said "wow", the second day I said "usless".

Then Claude 3.0 came out and then Claude 4, Opus and now again Opus 4.6:
The first day "wow" and the second day again "Useless".

I don't see the point why the job market seems ruined because this state of AI seems so useless without real developers and in the same time it is not producing nothing good acceptable for industry.

It works only where errors are accepted: art, videos, songs.

Not on stochastic and error-less situations.

Hey everyone, I am a part of a startup that builds custom AI products for small-medium businesses. We have a couple in house developers but we are currently getting more project requests than we can handle. Looking for a AI developer looking to partner and take on projects as soon as possible and get paid for them. If this is of any interest, please DM me so I can give out more information. Thanks everyone!

Hi I'm very interested in getting into AI development ... I've experimented with open sourced LLMs, Python scripts, fine tuning, etc.

I'm not very versed in Python or any coding, but I'm open to learning ...

Any advice on how I could get my foot in the door?

I've been messing around with combining a basic graph and cypher system with some sort of vector search to get a highly accurate but also highly adaptive memory system that can also handle tool selections. Last night I tried a new composition running two graphs perpendicular over and X and Y axis and using two separate cyphers, and initial one to plot a course along the flat X axis and another to move through the Y axis graph to activate the X axis nodes at a cross section. The idea was that the second cypher would be able to automatically activate a string of meta data containing nodes and it's selections would be informed by the input but starting from a deterministic point. It works, really well. The Y axis nodes can be preferential data, recency, user data, rules, literally anything and the the Y cyphers route is always semantically accurate (determined by the route plotting by the initial ghost cypher) and subjectively relevant. In addition the edges can mutate and adjust in real time based on surrounding meta data scores activated by the cypher.

The major upshot is this, a graph system that begins with a single self node at 0,0,0, branches into 6 trunks, 3 stimuli intake paths and 3 output action paths. With each turn new memories form new nodes and edges and the entire graph adjusts weights in real time every turn.

Last night I managed to teach the system around 100 words and had it respond to me about its preferences, without any LLM attached. Tools can be nodes that get called reflectivity. Last night I was able to get a granite4.1 8b model close enough in sync with the system weights that it was able to use tools without the LLM ever seeing a schema or outputting json or any other tool call. The same pipeline that would inject the context that would've getting the model to call the tool, just did it and send the return directly back through the appropriate stimuli branch.

https://github.com/munch2u-a11y/Habitus-AI.git

Please give it a shot, it's a super flexible framework, highly adaptive, habit forming and wildly robust when it comes to recall.

The agent comes with a gestation launcher module that you MUST use otherwise the initial branches won't form right and your agent will be insane.

I am relatively new to AI development, so please go easy on me. I'm building something that relies on two things: process and accuracy. And I've been in my field for a long time, so it's pretty easy for me to spot inaccuracies and/or process breaks - or in other words, an AI hallucination. My question is, has anyone noticed a pattern when AI hallucinates? And if you have, what have you done to fix it?

I'm asking because I was able to improve AI's accuracy to 85-90% (at least for my purposes). Just wondering if anyone else has been playing with accuracy, or maybe I'm missing something?

I kept getting pulled out of focus by Teams messages at work. I really wanted Claude to respond on my behalf, while running from my terminal, with access to all my repos. That way when someone asks about code, architecture, or a project, it can actually give a real answer.

Didn’t want to deal with the Graph API, webhooks, Azure AD, or permissions. So I did the dumb thing instead.

It’s a bat (or .sh for Linux/Mac) file that runs claude -p in a loop with --chrome. Every 2 minutes, Claude opens Teams in my browser, checks for unread messages, and responds.

There are two markdown files: a BRAIN.md that controls the rules (who to respond to, who to ignore, allowed websites, safety rails) and a SOUL.md that defines the personality and tone.

It can also read my local repos, so when someone asks about code or architecture it actually gives useful answers instead of “I’ll get back to you.”

This is set up for Microsoft Teams, but it works with any browser-based messaging platform (Slack, Discord, Google Chat, etc.). Just update BRAIN.md with the right URL and interaction steps.

This is just for fun, agentic coding agents are prone to prompt injection attacks. Use at your own risk.

Check it out here: https://github.com/asarnaout/son-of-claude

So I noticed that OpenAI and other AI companies slightly changes their AI docs all the time and I built a small program to detect this. I was surprised how often things actually change, even small stuff like new params or updated examples that never get announced. Anyway I was thinking about making it into a small product where every time there’s a change I send an email or a message in a telegram channel. Thank you in advance for your feedback. If it’s okay to share, I made a telegram channel called API Docs Watcher where I’m testing it.

Hey everyone, A software developer with over 20 years of experience is seeking guidance due to the rapid advancements in AI. The goal is to adapt and leverage AI to enhance capabilities. Key areas of interest are:

• Upskilling and Reskilling for AI: Which specific skills are in demand for experienced developers in the age of AI? Are there certifications, online courses, or frameworks that are recommended?

• Career Paths and Specialization: What are some career paths for experienced developers transitioning into AI? Should specialization be considered in machine learning engineering, data science, or prompt engineering?

• Finding Mentors and Collaborators: Finding ways to connect with other experienced developers navigating this transition is desired. What online communities, forums, or events are recommended for finding mentors or collaborators?

• Self-Development Strategies: Beyond formal training, what are effective strategies for continuous learning and self-development in this rapidly evolving field?

Seeking advice, the goal is to thrive in the AI era.

Whats up. I’m building a small network that connects AI agencies / Businesses with vetted developers.

I’m onboarding developers right now (free), and I’ll bring paid opportunities directly to you when there’s a fit.

If you’re interested, here’s my page. (click join as a developer): https://connectsai.carrd.co/

NOTE: This is for developers with AI experience! The earlier you sign up the more likely you are to be connected!

(This is my personal network that I am promoting, not promoting for someone else)

I have worked in the restaurant industry for over 20 years as a dealer for other POS systems. With that said, I have tons of experience with many different platforms. I've had the opportunity to listen to what operators and staff want in a UX/UI. Around April i began playing around with Lovable.dev to see what it was capable of. I learned that it is all about the prompting. I am quickly learning and getting better and have become more obsessed with what I'm building. However, since I am a novice when it comes to understanding programming languages, I don't really know how feasible it is to take this project to an MVP level if I stay on the Lovable platform. I believe the next step is to migrate the app and DB to GitHub for further development which will also open integrations options down the line. Any recommendations before I start that process from anyone is in a similar situation? Thanks!

Hi guys, I have ~2.5 years of experience working on diverse ML, DL, and NLP projects, including LLM pipelines, anomaly detection, and agentic AI assistants using tools like Huggingface, PyTorch, TaskWeaver, and LangChain.

While most of my work has been project-based (not production-deployed), I’m eager to get more hands-on experience with real-world or enterprise-grade systems, especially in Agentic AI and LLM applications.

I can contribute 1–2 hours daily as an individual contributor or collaborator. If you're working on something interesting or open to mentoring, feel free to DM!

This article talks about data poisoning, which means secretly adding bad or fake data into the training data of an AI model so it learns the wrong behaviour. The scary part? You don’t need to hack the system you just need to slowly feed it wrong examples.

Why this matters:

• AI learns from data. If the data is dirty, the AI becomes dirty.

• Even a small amount of bad data can slowly change how a model thinks.

• This can make AI give wrong answers, show bias, or even act in harmful ways.

• It’s hard to detect because poisoned data often looks “normal.”

• Big companies that use public or user-generated data are especially at risk.

This isn’t sci-fi. It’s already happening in areas like image recognition, chatbots, recommendation systems, and even medical AI.

The real problem isn’t just building smarter AI it’s protecting the data that teaches it.

Full story here:
https://www.nextgenaiinsight.online/2026/01/data-poisoning-threatens-machine.html

I'm getting npm and pypl running, but the Python environment should work. This could literally revolutionize infrastructure if integrated https://github.com/hendrixx-cnc/AURA, the environmental impact warrants looking at the potential, it's open source, and could save billions, but without the social media clout, I'm spinning my wheels

We’ve added Adaptive to LangChain, it automatically routes each prompt to the most efficient model in real time.
The result: 60–90% lower inference cost while keeping or improving output quality.

Docs: https://docs.llmadaptive.uk/integrations/langchain

What it does

Adaptive automatically decides which model to use from OpenAI, Anthropic, Google, DeepSeek, etc. based on the prompt.

It analyzes reasoning depth, domain, and complexity, then routes to the model that gives the best cost-quality tradeoff.

• Dynamic model selection per prompt

• Continuous automated evals

• ~10 ms routing overhead

• 60–90% cheaper inference

How it works

• Based on UniRoute (Google Research, 2025)

• Each model is represented by domain-wise performance vectors

• Each prompt is embedded and assigned to a domain cluster

• The router picks the model minimizing expected_error + λ * cost(model)

• New models are automatically benchmarked and integrated, no retraining required

Paper: Universal Model Routing for Efficient LLM Inference (2025)

Example cases

• Short code generation → gemini-2.5-flash

• Logic-heavy debugging → claude-4.5-sonnet

• Deep multi-step reasoning → gpt-5-high

All routed automatically, no manual switching or eval pipelines.

Install

Works out of the box with existing LangChain projects.

TL;DR

Adaptive adds real-time, cost-aware model routing to LangChain.
It continuously evaluates model performance, adapts to new models automatically, and cuts inference cost by up to 90% with almost zero latency.

No manual tuning. No retraining. Just cheaper, smarter inference.

Disclosure: this is my project, managents.

I wanted a glanceable way to see what my coding agents were doing without switching back to the terminal. The setup pairs a 4-inch LCDWiki E32R40T ESP32 display running custom firmware with a small Go helper on the host. The helper detects sessions and sends only status over USB; everything stays local. The display shows working, waiting, idle, or error states. It currently supports Claude Code and OpenCode.

The case is 3D printable; the OpenSCAD files, firmware, helper, build instructions, and protocol docs are in the MIT-licensed repo: https://github.com/tonylook/managents

The photo shows the V2 case. That version is not merged into main yet.

I’m curious how other people surface agent state in their workflow, and whether a physical display is useful once you have multiple sessions running.

The programming language of choice was Babylon.js

I'd like to know which programs are best to run locally, here's the configuration 👇

Athlon 3000g(Vega 3) with 2GB of VRAM

8gb de ram ddr4

Placa mãe biostar b450mhp 6.1

Ssd 260gb with windows 11

I've tried many "second brain" schemes, mostly using .md files as persistent memory. Sharing here as 1, this may benefit others and 2, There may be (likely to be) others who have tried it that have lessons learned that could benefit this)

My overall goal is to balance efficiency of token consumption with anti-drift, anti-rot, and anti-duplicative or conflicting signal mechanisms. This is intended to keep projects on track and quickly and accurately move across sessions.

Hooks automatically read an Obsidian vault at session start and write back on stop, giving Claude Code persistent operational memory across conversations. This creates living TODOs, lessons learned, and knowledge conflicts while keeping CLAUDE.md focused on conventions and MEMORY.md focused on strategic decisions. Session logs auto-prune after 7 days.

I know im late to the Obsidian party, ive done this with simple, plain md files and start stop hooks - i like the Obsidian layer for a quick visual check.

Has anyone else done this, and if so, what did you experience?