title: 4x AMD R9700 (128GB VRAM) + Threadripper 9955WX Build

summary:
Disclaimer: I am from Germany and my English is not perfect, so I used an LLM to help me structure and write this post.

Context & Motivation: I built this system for my small company. The main reason for all new hardware is that I received a 50% subsidy/refund from my local municipality for digitalization investments. To qualify for this funding, I had to buy new hardware and build a proper "server-grade" system.

My goal was to run large models (120B+) locally for data privacy. With the su

genesis_applicability: Knowledge retrieval / Memory, Prompt engineering / Axioms, Cost optimization / Budget

action_items: Evaluate new tool/model for Genesis integration

action_items: Compare against current Genesis performance


title: Microsoft pauses Claude Code rollout after Satya intervention

summary:
Following up on [my earlier post](https://www.reddit.com/r/ClaudeAI/comments/1q6rimw/even_microsoft_employees_started_using_claude_code/) \- Microsoft has officially paused further Claude Code deployment across the company after guidance from Satya and senior leadership.

Employees are now being directed to use GitHub Copilot. The internal messaging claims Copilot has "mostly closed the gaps" with Claude Code.

Exceptions exist for "high-priority R&D" who can still get Anthropic API access w

genesis_applicability: General AI advancement

action_items: Review source code for implementation patterns


title: Is Local Coding even worth setting up

summary:
Hi I am new to Local LLM  but have been having a lot of issues setting up a local LLM coding environment so wanted some suggestions from people.I have a 5070 ti (16gb vram).

I have tried to use Kilo code with qwen 2.5 coder 7B running through ollama but the context size feels so low that it finishes the context within a single file of my project.

How are other people with a 16gb GPU dealing with local llm?

genesis_applicability: Local deployment / Ollama


title: 3x3090 + 3060 in a mid tower case

summary:
Decided to go all out and max out this desktop. I was lucky to find 3090 cards for around 600 usd, over a period of 3 months and decided to go for it.

The RAM was a bit more expensive, but I had 64 bought before the price spiked.

I didn’t want to change the case, because I through it’s a high quality case and it would be a shame to toss it. So made the most out of it!

Specs:
* Fractal Define 7 Mid Tower
* 3x3090 + 1x3060 (86gb total, but 72gb VRAM main)
* 128GB DDR4 (Corsair 4x32)
* Corsair

genesis_applicability: Performance / Latency


title: ShapeR: Robust Conditional 3D Shape Generation from Casual Captures

summary: Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely met in real-world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Given an image sequence, we leverage off-the-shelf visual-inertial SLAM, 3D detection algorithms, and vision-language models to extract, for each object, a set of sparse SLAM poi

key_findings: Given an image sequence, we leverage off-the-shelf visual-inertial SLAM, 3D detection algorithms, and vision-language models to extract, for each object, a set of sparse SLAM points, posed multi-view images, and machine-generated captions

genesis_applicability: Knowledge retrieval / Memory

related_topics: arxiv paper


title: Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs

summary: Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures. In this paper, we extend our prior LLM-based Predictive Process Monitoring framework, which was initially focused on total time prediction via prompting. The extension consists of comprehensively evaluating its generality, semantic leverage, and reasoning mechanisms, also across multiple Key Performance Indicators. Emp

key_findings: Empirical evaluations conducted on three distinct event logs and across the Key Performance Indicators of Total Time and Activity Occurrence prediction indicate that, in data-scarce settings with only 100 traces, the LLM surpasses the benchmark methods

genesis_applicability: Knowledge retrieval / Memory, Prompt engineering / Axioms


title: My gpu poor comrades, GLM 4.7 Flash is your local agent

summary:
I tried many MoE models at 30B or under and all of them failed sooner or later in an agentic framework. If z.ai is not redirecting my requests to another model, then GLM 4.7 Flash is finally the reliable (soon local) agent that I desperately wanted.

I am running it since more than half an hour on opencode and it produced hundreds of thousands tokens in one session (with context compacting obviously) without any tool calling errors. It clones github repos, it runs all kind of commands, edits fil

genesis_applicability: Agentic workflows / RWL, Local deployment / Ollama


title: I made a Top-K implementation that's up to 20x faster than PyTorch CPU (open source)

summary:
Spent way too long optimizing Top-K selection for LLM sampling and finally hit some stupid numbers.

**TL;DR:** AVX2-optimized batched Top-K that beats PyTorch CPU by 4-20x depending on vocab size. Sometimes competitive with CUDA for small batches.

**Benchmarks (K=50):**

* Vocab=32K: 0.043ms vs PyTorch's 0.173ms (4x faster)
* Vocab=128K: 0.057ms vs PyTorch's 0.777ms (13x faster)
* Vocab=256K: 0.079ms vs PyTorch's 1.56ms (20x faster)

Integrated it into llama.cpp and got 63% faster prompt proce

genesis_applicability: Prompt engineering / Axioms


title: Models that run in 72GB VRAM with context loaded in GPU (3x3090 benchmark test)

summary:
I recently finished my 3x3090 setup, and thought of sharing my experience.

This is very much a personal observation, with some very basic testing.

The benchmark is by no means precise, however, after checking the numbers, it is very much aligned with "how I feels they perform" after a few days of bouncing between them. All the above are running on CUDA 12 llama.cpp via LM Studio (nothing special).

**1. Large models (> 100 B)**

All big models run in roughly the same ballpark—a


title: I experimented with a Figma-style canvas to run multiple Claude Code Agents in parallel. What do you think? Github repo below

summary:
Hi community,

I built a Figma-like canvas to run and monitor multiple coding agents in parallel. I didn't like how current IDEs handle many agents next to each other.

Forking and branching agent context is also super easy with drag and drop.

I often had problems orchestrating multiple agents using the current IDEs because i had to reread the context to understand what each agent does and why i started the agent.

I like the canvas because it gives me a spatial component to group my agents w

genesis_applicability: Agentic workflows / RWL, Knowledge retrieval / Memory, Local deployment / Ollama


title: WSJ: Claude Code is Taking the AI World by Storm - Boris answered few questions

summary:
Boris' answers in comment
extract:
**Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away**

*By Bradley Olson, January 17th, 2026, 12pm Eastern time*

They call it getting **"Claude Pilled."** It's the moment software engineers, executives and investors turn their work over to Anthropic's Claude AI and then witness a thinking machine of shocking capability—even in an age awash in powerful AI tools.

Many coders spent their holiday breaks on a "Claude Bender" testing out

key_findings: Many coders spent their holiday breaks on a "Claude Bender" testing out the capabilities of the latest Anthropic model, **Claude Opus 4

key_findings: 5**, which they used within a desktop coding tool called **Claude Code**


title: Real Alternative/Supplement for Opus 4.5?

summary:
I've been using the Claude Max 20x Plan since the day it was released and on every week since ended at around 60-70% depending on what I've been doing at work. As everyone knows that has looked at GitHub, since after the Christmas/Holiday promotion limits are hit way faster.

These days I usually hit my 100% after around 4-5 days, so I would say the limit is down something like 30% compared to pre-Holiday. I experience the same issue with 2.0.76 and older and the latest Claude Code, already done


title: Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning

summary: Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritization of which facilities to upgrade to maximize population coverage while accounting for diverse expert and stakeholder preferences. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we propose a hybrid framework that systematically integrates expert knowledge with optimization techniq

key_findings: To bridge these domains, we develop the Large language model and Extended Greedy (LEG) framework


title: MHA2MLA-VLM: Enabling DeepSeek's Economical Multi-Head Latent Attention across Vision-Language Models

summary: As vision-language models (VLMs) tackle increasingly complex and multimodal tasks, the rapid growth of Key-Value (KV) cache imposes significant memory and computational bottlenecks during inference. While Multi-Head Latent Attention (MLA) offers an effective means to compress the KV cache and accelerate inference, adapting existing VLMs to the MLA architecture without costly pretraining remains largely unexplored. In this work, we present MHA2MLA-VLM, a parameter-efficient and multimodal-aware f

key_findings: Furthermore, we introduce parameter-efficient fine-tuning to minimize adaptation cost and demonstrate that minimizing output activation error, rather than parameter distance, substantially reduces performance loss

genesis_applicability: Cost optimization / Budget