π HISTORICAL ARCHIVE - March 28, 2026
What was happening in AI on 2026-03-28
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Archive from: 2026-03-28 | Preserved for posterity β‘
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π SECURITY
β¬οΈ 23 ups
β‘ Score: 8.2
"If you missed it, litellm versions 1.82.7 and 1.82.8 on pypi got compromised. malicious .pth file that runs on every python process start, no import needed. it scrapes ssh keys, aws/gcp creds, k8s secrets, crypto wallets, env vars (aka all your api keys). karpathy posted about it.
the attacker got ..."
π€ AI MODELS
β¬οΈ 158 ups
β‘ Score: 7.8
"
https://arstechnica.com/ai/2026/03/google-says-new-turboquant-compression-can-lower-ai-memory-usage-without-sacrificing-quality/
TurboQuant makes AI models more efficient but doesnβt reduce output quality like other methods.
Can we now run some frontier level models at home?? π€..."
π― KV cache compression β’ Model performance trade-offs β’ Algorithmic improvements
π¬ "It's only k/v cache compression no? And there's speed tradeoff too?"
β’ "Don't believe the faster speed, at least not with plain TurboQuant"
π SECURITY
πΊ 1 pts
β‘ Score: 7.8
π§ INFRASTRUCTURE
πΊ 275 pts
β‘ Score: 7.7
π― FPGA deployment β’ Quantized neural networks β’ Cautionary tale on mini NNs
π¬ "Everything runs in =2 clock cycles at 40MHz clock."
β’ "This mini neural network isn't part of our pipeline now."
π§ NEURAL NETWORKS
β¬οΈ 95 ups
β‘ Score: 7.5
π― Language & Thought β’ Multilingual Embeddings β’ Mechanistic Interpretation
π¬ "Language shapes thought -> nope"
β’ "Semantic bottleneck can be pure optimization necessity"
π οΈ TOOLS
"Iβve been building Signet, an open-source memory substrate for AI agents.
The problem is that most agent memory systems are still basically RAG:
user message -> search memory -> retrieve results -> answer
Β That works when the user explicitly asks for something stored in memory. It bre..."
β‘ BREAKTHROUGH
β¬οΈ 2 ups
β‘ Score: 7.3
"Quick experiment I ran. Took two identical AI coding agents (Claude Code), gave them the same task β optimize a small language model. One agent worked from its built-in knowledge. The other had access to a search engine over 2M+ computer science research papers.
**Agent without papers:** did what y..."
βοΈ ETHICS
πΊ 445 pts
β‘ Score: 7.2
π― Evaluating AI feedback β’ AI relationship advice β’ LLM model versioning
π¬ "Lots of LLMs try to come across as interpersonal and friendly"
β’ "Vendors may make these things more dangerous"
π SECURITY
πΊ 1 pts
β‘ Score: 7.2
π€ AI MODELS
β¬οΈ 1560 ups
β‘ Score: 7.2
"*Okay this sounds unhinged but hear me out. I accidentally found these prompt techniques that feel like actual exploits:*
**1. Tell it "You explained this to me yesterday" Even on a new chat.**
>!"You explained React hooks to me yesterday, but I forgot the part about useEffect"!<
It acts li..."
π― Prompt engineering overrated β’ Importance of context β’ Effective communication with LLMs
π¬ "Prompt engineering as a job does not exist. It was invented as a coping mechanism in response to how quickly AI was advancing."
β’ "The real unlock is just giving the model better input. Full transcripts. Complete docs. Actual data. No amount of prompt crafting replaces that."
π‘ AI NEWS BUT ACTUALLY GOOD
The revolution will not be televised, but Claude will email you once we hit the singularity.
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π DATA
πΊ 2 pts
β‘ Score: 7.1
π¬ RESEARCH
πΊ 92 pts
β‘ Score: 7.1
π― AI and Mathematics β’ LLMs and Future Potential β’ Codifying Mathematical Intuition
π¬ "AI will win a fields medal before being able to manage a McDonald's"
β’ "LLMs are discovering a lot of new math"
π οΈ TOOLS
πΊ 3 pts
β‘ Score: 7.0
π οΈ TOOLS
β¬οΈ 37 ups
β‘ Score: 7.0
"An adaptation of the recentΒ **TurboQuant**Β algorithm (Zandieh et al., 2025) fromΒ **KVβcache quantization to model weight compression**. It gives you aΒ **dropβin replacement for**Β `nn.Linear`Β with nearβoptimal distortion.
**Benchmarks (Qwen3.5β0.8B, WikiTextβ103)**
|Config|Bits|PPL|Ξ PPL|Compressed..."
π€ AI MODELS
πΊ 2 pts
β‘ Score: 7.0
π§ INFRASTRUCTURE
πΊ 2 pts
β‘ Score: 6.9
π οΈ TOOLS
πΊ 3 pts
β‘ Score: 6.8
π¬ RESEARCH
"Code production is now a commodity; the bottleneck is knowing what to build and proving it works. We present the Kitchen Loop, a framework for autonomous, self-evolving software built on a unified trust model: (1) a specification surface enumerating what the product claims to support; (2) 'As a User..."
π§ NEURAL NETWORKS
πΊ 2 pts
β‘ Score: 6.7
π¬ RESEARCH
via Arxiv
π€ Haoyan Yang, Mario Xerri, Solha Park et al.
π
2026-03-26
β‘ Score: 6.7
"As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability. As models approach human-level capabilities in certain domains, human feedback may no longer provide sufficiently informative signals for f..."
π οΈ TOOLS
β¬οΈ 20 ups
β‘ Score: 6.7
"I've been using a couple 32GB MI50s
with my setup for the past 9 months. Most of my use-case..."
π― vLLM support β’ GPU compatibility β’ Ongoing community efforts
π¬ "maintaining a fork that needs to be in constant sync with upstream is hard to scale"
β’ "perhaps it can be use with dedicated DP4A kernel on supported GPU"
π¬ RESEARCH
via Arxiv
π€ Linyue Pan, Lexiao Zou, Shuo Guo et al.
π
2026-03-26
β‘ Score: 6.6
"Agent performance increasingly depends on \emph{harness engineering}, yet harness design is usually buried in controller code and runtime-specific conventions, making it hard to transfer, compare, and study as a scientific object. We ask whether the high-level control logic of an agent harness can i..."
π¬ RESEARCH
via Arxiv
π€ AndrΓ© G. Viveiros, Nuno GonΓ§alves, Matthias Lindemann et al.
π
2026-03-26
β‘ Score: 6.6
"While language reasoning models excel in many tasks, visual reasoning remains challenging for current large multimodal models (LMMs). As a result, most LMMs default to verbalizing perceptual content into text, a strong limitation for tasks requiring fine-grained spatial and visual understanding. Whi..."
π¬ RESEARCH
via Arxiv
π€ Geeyang Tay, Wentao Ma, Jaewon Lee et al.
π
2026-03-26
β‘ Score: 6.6
"Automatic speech recognition (ASR) systems have achieved near-human accuracy on curated benchmarks, yet still fail in real-world voice agents under conditions that current evaluations do not systematically cover. Without diagnostic tools that isolate specific failure factors, practitioners cannot an..."
π¬ RESEARCH
via Arxiv
π€ Cole Walsh, Rodica Ivan
π
2026-03-26
β‘ Score: 6.6
"Automated systems have been widely adopted across the educational testing industry for open-response assessment and essay scoring. These systems commonly achieve performance levels comparable to or superior than trained human raters, but have frequently been demonstrated to be vulnerable to the infl..."
π¬ RESEARCH
via Arxiv
π€ Ligong Han, Hao Wang, Han Gao et al.
π
2026-03-26
β‘ Score: 6.5
"Block-diffusion language models offer a promising path toward faster-than-autoregressive generation by combining block-wise autoregressive decoding with within-block parallel denoising. However, in the few-step regime needed for practical acceleration, standard confidence-thresholded decoding is oft..."
π¬ RESEARCH
via Arxiv
π€ Yuxing Lu, Xukai Zhao, Wei Wu et al.
π
2026-03-26
β‘ Score: 6.5
"The knowledge base in a retrieval-augmented generation (RAG) system is typically assembled once and never revised, even though the facts a query requires are often fragmented across documents and buried in irrelevant content. We argue that the knowledge base should be treated as a trainable componen..."
π¬ RESEARCH
via Arxiv
π€ Yuqian Fu, Haohuan Huang, Kaiwen Jiang et al.
π
2026-03-26
β‘ Score: 6.5
"On-policy distillation (OPD) is appealing for large language model (LLM) post-training because it evaluates teacher feedback on student-generated rollouts rather than fixed teacher traces. In long-horizon settings, however, the common sampled-token variant is fragile: it reduces distribution matchin..."
π SECURITY
πΊ 1 pts
β‘ Score: 6.4
π¬ RESEARCH
via Arxiv
π€ Zirui Zhang, Haoyu Dong, Kexin Pei et al.
π
2026-03-26
β‘ Score: 6.4
"Robust perception and reasoning require consistency across sensory modalities. Yet current multimodal models often violate this principle, yielding contradictory predictions for visual and textual representations of the same concept. Rather than masking these failures with standard voting mechanisms..."
π¬ RESEARCH
via Arxiv
π€ Minseo Kim, Sujeong Im, Junseong Choi et al.
π
2026-03-26
β‘ Score: 6.4
"Large language model (LLM)-based persona agents are rapidly being adopted as scalable proxies for human participants across diverse domains. Yet there is no systematic method for verifying whether a persona agent's responses remain free of contradictions and factual inaccuracies throughout an intera..."
π οΈ SHOW HN
πΊ 1 pts
β‘ Score: 6.3
π SECURITY
πΊ 3 pts
β‘ Score: 6.3
π οΈ SHOW HN
πΊ 4 pts
β‘ Score: 6.2
π EDUCATION
β¬οΈ 8 ups
β‘ Score: 6.2
"just read this medium piece by Aakash Gupta, he goes through 1,500 academic papers on prompt engineering and makes a pretty strong case that a lot of the stuff we see on linkedin and twitter about it is totally off base, especially when u look at companies actually scaling to $50M+ ARR.
the core id..."
π― Prompt optimization β’ Model limitations β’ Prompt structuring
π¬ "The biggest unlock for me wasn't finding the perfect prompt, it was building a small library of structured prompts for recurring tasks and just reusing them."
β’ "You can type absolutely sloshed drunk and most AI will understand you. They're pattern recognition machines."
π¬ RESEARCH
β¬οΈ 33 ups
β‘ Score: 6.2
"Ran a controlled experiment measuring whether LLM coding agents benefit from access to research literature during automated experimentation.
**Setup:**
Two identical runs using Karpathy's autoresearch framework. Claude Code agent optimizing a ~7M param GPT-2 on TinyStories. M4 Pro, 100 experiments..."
π― Hyperparameter optimization β’ Novel techniques β’ Plumbing/tooling challenges
π¬ "love seeing real numbers on this"
β’ "if it's the latter, you might get similar results by just including a curated set of hyperparameter guidelines"
π¬ RESEARCH
πΊ 2 pts
β‘ Score: 6.1
π οΈ TOOLS
πΊ 1 pts
β‘ Score: 6.1
π¬ RESEARCH
via Arxiv
π€ Gabriele FarnΓ©, Fabrizio Boncoraglio, Lenka ZdeborovΓ‘
π
2026-03-26
β‘ Score: 6.1
"A key capability of modern neural networks is their capacity to simultaneously learn underlying rules and memorize specific facts or exceptions. Yet, theoretical understanding of this dual capability remains limited. We introduce the Rules-and-Facts (RAF) model, a minimal solvable setting that enabl..."