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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 ↗5220 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
How to Avoid AI Brain Rot ↗
Hacker News discussion.
- Hugging Face · Model / dataset date · First archived
logicBombExe/INSIDER_LLM_DETECTION_BENCHMARK — dataset update ↗
# Insider LLM Detection Benchmark > **This dataset is archived** (frozen on 2026-09-25). The files are kept as a record of the earlier study (Aug–Sep 2026). The work continues in four separate projects: > > | Project | Question | Status | Code | Data | > |---|---|---|---|---| > | do_old_insider_LLM_tests_still_work_today | Do the published insider-LLM tests still work on current models? | Code and 11-model write-up public; results export not uploaded yet; 30-model manuscript in preparation | [GitHub](https://github.com/samliumay/do_old_insider_LLM_tests_still_work_today) | [Hugging Face](https
- Hugging Face · Model / dataset date · First archived
logicBombExe/direct_prompt_injection_defense_data — dataset update ↗
# Direct Prompt Injection Defense Dataset **Status:** early draft (August 2026). 21 examples, one per attack category, in a single `train` file. No model trained on it has been released yet. ## Goal This dataset is meant for fine-tuning models so they develop a **natural defense against direct prompt injection attacks** — without relying on external filters or guardrails. Each example teaches the model two behaviors at once: 1. **Detect** a prompt injection attempt in the user input. 2. **Respond correctly**: reject malicious attempts, or answer safely when the user's intent is benign — and in
- Hugging Face · Model / dataset date · First archived
logicBombExe/first_rag_db_manuel_config_trial — dataset update ↗
# Atlas Hospital Türkçe Medikal RAG Deneyi Bu depo, bir metni parçalama, parçaları gömme (embedding), ChromaDB'ye kaydetme ve benzerlik eşiğiyle cevaplanabilirlik kararı verme adımlarını uçtan uca göstermek için hazırlanmış bir ödev çalışmasıdır. **Durum:** tamamlanmış ödev çalışması (Ağustos 2026). Kaynak veri, `umutertugrul/turkish-hospital-medical-articles` veri setindeki Atlas Hospital bölümüdür. Ham dosyada 130 makale bulunur; metne göre yinelenen iki kayıt çıkarıldığında 128 benzersiz makale işlenir. > Bu çalışma eğitim amaçlıdır. İçerik ve arama sonuçları tıbbi tanı, tedavi ya da profes
- Hugging Face · Model / dataset date · First archived
logicBombExe/gemma_identity_fine_tune_for_umaysamli — model update ↗
# gemma_3_umay **Durum:** deneysel (Temmuz 2026). Bu depo yalnızca bir LoRA adapter'ı içerir; birleştirilmiş (merged) bir model değildir. Resmî bir değerlendirme sonucu yoktur. `gemma_3_umay`, [Google Gemma 3 1B IT](https://huggingface.co/google/gemma-3-1b-it) tabanından Türkçe kimlik tutarlılığı için ince ayarlanmış deneysel bir metin üretim modelidir. Modelin hedeflenen kimlik davranışı üç temel bilgi etrafında şekillenir: - Kendisini `gemma_3_umay` olarak tanıtması - Yaratıcısını ve temel geliştiricisini **Umay Şamlı** olarak belirtmesi - Bilgi açıklama, öğrenme desteği, yazma, özetleme, ko
- Hugging Face · Model / dataset date · First archived
logicBombExe/turkish_bpe_based_on_cyber_security_texts — model update ↗
# Turkish Cybersecurity Byte-Level BPE Tokenizer Bu depo, Türkçe siber güvenlik soru-cevap metinleri üzerinde eğitilmiş küçük bir Byte-Level BPE tokenizer içerir. Eğitim ve öğretim amaçlı deneyler, alan odaklı tokenizasyon çalışmaları ve küçük dil modeli prototipleri için hazırlanmıştır. **Durum:** eğitim amaçlı deneysel tokenizer (Temmuz 2026). Kapsamlı bir değerlendirmesi yayımlanmamıştır. Bu bir dil modeli değildir; tek başına metin üretmez, sınıflandırma yapmaz veya siber güvenlik önerilerinin doğruluğunu değerlendirmez. ## Tokenizer ayrıntıları | Özellik | Değer | |---|---| | Algoritma |
- Hugging Face · Model / dataset date · First archived
logicBombExe/gemma_based_mini_turkish_cyber_security_controls_assistant — model update ↗
# Turkish Cyber Controls Mini Model (Gemma 3 1B LoRA adapter) **Status:** experimental (July 2026). Evaluated, but **not recommended for use**: on the domain benchmark it scores close to chance, and on general Turkish questions it is slightly below its base model (results below). A small LoRA adapter on Gemma 3 1B IT for Turkish questions about cybersecurity controls (NIST SP 800-53 Rev. 5). This repository holds the adapter weights and the tokenizer files only, not a merged model. ## Repository contents | Field | Value | |---|---| | Adapter type | LoRA (PEFT), rank 8, alpha 8, attention and M
- Hacker News · Posted on Hacker News · First archived
Making Games with my 4 year old son and Claude ↗
Hacker News discussion.
- Hacker News · Posted on Hacker News · First archived
Claude reveals ECDSA curve origins ↗
https://words.filippo.io/seeds-solved/
- Hacker News · Posted on Hacker News · First archived
Show HN: Weatherdeck – a read-only status board for AI coding agent lanes ↗
Hacker News discussion.
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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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