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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5395 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hacker News · Posted on Hacker News · First archived
Show HN: Prokop – Claude Code and Codex in one workspace, with shared memory ↗
Hacker News discussion.
- arXiv · Published · First archived
FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation ↗
Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering strong privacy benefits. However, this setting is highly vulnerable to backdoor attacks, in which malicious clients manipulate their updates to induce targeted behaviors while maintaining normal performance on benign inputs. In this work, we make a key observation specific to federated LLM fine-tuning: malicious clients tend to exhibit strong consistency in their model updates, whereas benign clients produce more diverse and dispersed updates. This st…
- Hacker News · Posted on Hacker News · First archived
AI Swarm Portal Guard – Local secret and shell-abuse check for VS Code ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
Arm/deit-tiny-int8-xnnpack-executorch — model update ↗
# DeiT-Tiny optimized for Arm-based Edge Linux DeiT-Tiny optimized with INT8 post-training quantization and exported to ExecuTorch `.pte` format for efficient image classification inference on Arm-based Edge Linux systems. ## Summary DeiT-Tiny optimized with INT8 post-training quantization for most eligible operations, while patch-embedding operations remain in FP32, and exported to ExecuTorch `.pte` format for efficient image classification inference on Arm-based Edge Linux systems. This repository contains an Arm-optimized version of facebook/deit-tiny-patch16-224 for image classification. T
- Hugging Face · Model / dataset date · First archived
Arm/deit-tiny-int8-litert — model update ↗
# DeiT-Tiny Patch16-224 optimized for Arm-based mobile CPUs with SME2 DeiT-Tiny Patch16-224 optimized with INT8 static post-training quantization for image classification, exported to LiteRT `.tflite` format for efficient inference on Arm-based mobile CPUs with SME2. ## Summary This repository contains an Arm-optimized version of facebook/deit-tiny-patch16-224 for image classification, quantized to INT8 via static post-training quantization — affine per-tensor activations, symmetric per-channel weights — while patch embeddings remain in FP32. The model is provided in LiteRT `.tflite` format, t
- Hugging Face · Model / dataset date · First archived
Arm/deit-tiny-int8-ethos-u-executorch — model update ↗
# DeiT-Tiny optimized for Arm-based Ethos-U NPU DeiT-Tiny image classification optimized as a quantized ExecuTorch `.pte` model for Arm-based Ethos-U NPU systems (Ethos-U85). Most quantized layers use INT8 activations and INT8 weights (a8w8), while selected output.dense layers use INT16 activations and INT8 weights (a16w8) to preserve accuracy within acceptable ranges. Quantization is per-tensor throughout: weights use symmetric quantization (zero-point fixed at 0) across all layers, while a8w8 activations use an asymmetric scheme (affine, histogram-observer-derived zero-point); the a16w8-over
- Hugging Face · Model / dataset date · First archived
Arm/clip-vit-base-patch32-int8-xnnpack-executorch — model update ↗
# openai/clip-vit-base-patch32 optimized for Arm-based Edge Linux systems An INT8-quantized version of openai/clip-vit-base-patch32 for zero-shot image classification, exported to ExecuTorch (`.pte`) and optimized for Arm-based Edge Linux systems. ## Summary This repository contains an Arm-optimized version of openai/clip-vit-base-patch32 for zero-shot image classification. The vision encoder is quantized to dynamic INT8 — per-channel symmetric weights, dynamic INT8 activations computed at inference time — while the text encoder remains FP32, because PT2E could not capture its causal attention
- Hacker News · Posted on Hacker News · First archived
Show HN: GPT-Load – A self-hosted AI gateway with reversible redaction ↗
Hacker News discussion.
- GitHub · Repository created · First archived
Zfinix/eleven ↗
Agent-first reverse engineering for macOS, Windows and Linux: any ACP agent reads, renames and patches binaries, Android apps and GPU kernels next to you.
- Hacker News · Posted on Hacker News · First archived
Show HN: Mana – family organizer with an AI assistant ↗
Hi HN, I built Mana, a family organizer: shared calendars, shopping lists, receipt scanners, meal planning, chores, and notes for the whole household, with a built-in AI assistant ("add milk and eggs," "plan dinners for the week") plus automations (e.g., milk goes back on the list before it runs out). Additionally, you can connect Mana MCP to your Claude, ChatGPT, Gemini etc. The goal is to eventually take the model calls and storage to local but…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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