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1000 results for “context”
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Context Warning:
Abuse, Murder, Stalkinghttps://www.change.org/p/royal-commission-into-the-killing-of-australian-women-and-girls/u/34380769?
Femicide Watch Australia
https://australianfemicidewatch.org/#coerciveControl #domesticViolence #FamilyViolence #Femicide
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Context Warning:
Abuse, Murder, Stalkinghttps://www.change.org/p/royal-commission-into-the-killing-of-australian-women-and-girls/u/34380769?
Femicide Watch Australia
https://australianfemicidewatch.org/#coerciveControl #domesticViolence #FamilyViolence #Femicide
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Context Warning:
Abuse, Murder, Stalkinghttps://www.change.org/p/royal-commission-into-the-killing-of-australian-women-and-girls/u/34380769?
Femicide Watch Australia
https://australianfemicidewatch.org/#coerciveControl #domesticViolence #FamilyViolence #Femicide
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Context Warning:
Abuse, Murder, Stalkinghttps://www.change.org/p/royal-commission-into-the-killing-of-australian-women-and-girls/u/34380769?
Femicide Watch Australia
https://australianfemicidewatch.org/#coerciveControl #domesticViolence #FamilyViolence #Femicide
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Context Warning:
Abuse, Murder, Stalkinghttps://www.change.org/p/royal-commission-into-the-killing-of-australian-women-and-girls/u/34380769?
Femicide Watch Australia
https://australianfemicidewatch.org/#coerciveControl #domesticViolence #FamilyViolence #Femicide
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1M context is now generally available for Opus 4.6 and Sonnet 4.6 | Claude claude.com/blog/1m-contex… #AI #Claude #ContextWindow
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AUCTeX, the TeX mode for Emacs, got a few patches to enhance the ConTeXt experience, see https://mailman.ntg.nl/archives/list/n[email protected]/thread/KBHWORCIXQNJSQ3KC4OF2F4EL2OROPJ6/
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AUCTeX, the TeX mode for Emacs, got a few patches to enhance the ConTeXt experience, see https://mailman.ntg.nl/archives/list/n[email protected]/thread/KBHWORCIXQNJSQ3KC4OF2F4EL2OROPJ6/
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AUCTeX, the TeX mode for Emacs, got a few patches to enhance the ConTeXt experience, see https://mailman.ntg.nl/archives/list/n[email protected]/thread/KBHWORCIXQNJSQ3KC4OF2F4EL2OROPJ6/
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AUCTeX, the TeX mode for Emacs, got a few patches to enhance the ConTeXt experience, see https://mailman.ntg.nl/archives/list/n[email protected]/thread/KBHWORCIXQNJSQ3KC4OF2F4EL2OROPJ6/
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At the ConTeXt intro workshop last Wednesday, participants struggled with ConTeXt on the newly released TeX Live 2026.
Not the usual about wrong paths or unwritable cache directories.ConTeXt was apparently correctly installed, generating the file database and the format was working, but then it couldn’t find the format file if the "context" or "mtxrun" command wasn’t called with its full path.
TeX Live’s processes were changed before this release and something’s broken.
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Contextual Entry Strategy dalam Trading (Bukan Sekadar Pola Chart)
#Tradingan - #Contextual Entry Strategy dalam Trading (Bukan Sekadar Pola Chart) - Dalam dunia #trading, banyak trader pemula memulai perjalanan mereka dengan mempelajari berbagai #pola chart. Pola seperti double top, double bottom, head and shoulders, atau berbagai #pola candlestick sering dianggap sebagai sinyal utama untuk melakukan #entry. Banyak buku, video, dan materi edukasi trading juga…
https://tradingan.com/contextual-entry-strategy-dalam-trading-bukan-sekadar-pola-chart
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Contextual Entry Strategy dalam Trading (Bukan Sekadar Pola Chart)
#Tradingan - #Contextual Entry Strategy dalam Trading (Bukan Sekadar Pola Chart) - Dalam dunia #trading, banyak trader pemula memulai perjalanan mereka dengan mempelajari berbagai #pola chart. Pola seperti double top, double bottom, head and shoulders, atau berbagai #pola candlestick sering dianggap sebagai sinyal utama untuk melakukan #entry. Banyak buku, video, dan materi edukasi trading juga…
https://tradingan.com/contextual-entry-strategy-dalam-trading-bukan-sekadar-pola-chart
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Ваш CLAUDE.md делает агента тупее. Исследование на 138 репозиториях это доказало
Я написал CLAUDE.md на 200 строк. Исследование ETH Zurich на 138 репозиториях говорит: мой агент стал от этого тупее на 3%, а я плачу на 20% больше за токены. Разбираюсь, что пошло не так.
https://habr.com/ru/articles/1010160/
#CLAUDEmd #AGENTSmd #контекст #AIагенты #coding_agents #ETH_Zurich #context_engineering
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"Added 1M context window for Opus 4.6 by default for Max, Team, and Enterprise"
https://raw.githubusercontent.com/anthropics/claude-code/refs/heads/main/CHANGELOG.md
#HackerNews #Added #1M #context #window #for #Opus #4.6 #by #default #for #Max #Team #and #Enterprise
Opus4.6 #ContextWindow #Max #Team #Enterprise
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ContextHound v1.8.0 is out 🎉
This release adds a Runtime Guard API - a lightweight wrapper that inspects your LLM calls in-process, before the request hits OpenAI or Anthropic.
Free and open-source. If this is useful to you or your team, a GitHub star or a small donation helps keep development going.
github.com/IulianVOStrut/ContextHound#LLMSecurity #PromptInjection #CyberSecurity #OpenSource #AIRisk #AppSec #DevSecOps #GenAI #RuntimeSecurity #InfoSec #MLSecurity #ArtificialIntelligence
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ContextHound v1.8.0 is out 🎉
This release adds a Runtime Guard API - a lightweight wrapper that inspects your LLM calls in-process, before the request hits OpenAI or Anthropic.
Free and open-source. If this is useful to you or your team, a GitHub star or a small donation helps keep development going.
github.com/IulianVOStrut/ContextHound#LLMSecurity #PromptInjection #CyberSecurity #OpenSource #AIRisk #AppSec #DevSecOps #GenAI #RuntimeSecurity #InfoSec #MLSecurity #ArtificialIntelligence
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ContextHound v1.8.0 is out 🎉
This release adds a Runtime Guard API - a lightweight wrapper that inspects your LLM calls in-process, before the request hits OpenAI or Anthropic.
Free and open-source. If this is useful to you or your team, a GitHub star or a small donation helps keep development going.
github.com/IulianVOStrut/ContextHound#LLMSecurity #PromptInjection #CyberSecurity #OpenSource #AIRisk #AppSec #DevSecOps #GenAI #RuntimeSecurity #InfoSec #MLSecurity #ArtificialIntelligence
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ContextHound v1.8.0 is out 🎉
This release adds a Runtime Guard API - a lightweight wrapper that inspects your LLM calls in-process, before the request hits OpenAI or Anthropic.
Free and open-source. If this is useful to you or your team, a GitHub star or a small donation helps keep development going.
github.com/IulianVOStrut/ContextHound#LLMSecurity #PromptInjection #CyberSecurity #OpenSource #AIRisk #AppSec #DevSecOps #GenAI #RuntimeSecurity #InfoSec #MLSecurity #ArtificialIntelligence
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ContextHound v1.8.0 is out 🎉
This release adds a Runtime Guard API - a lightweight wrapper that inspects your LLM calls in-process, before the request hits OpenAI or Anthropic.
Free and open-source. If this is useful to you or your team, a GitHub star or a small donation helps keep development going.
github.com/IulianVOStrut/ContextHound#LLMSecurity #PromptInjection #CyberSecurity #OpenSource #AIRisk #AppSec #DevSecOps #GenAI #RuntimeSecurity #InfoSec #MLSecurity #ArtificialIntelligence
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Context Gateway – Compress agent context before it hits the LLM
https://github.com/Compresr-ai/Context-Gateway
#HackerNews #ContextGateway #CompressAgent #LLM #AItechnology #MachineLearning
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Context Gateway – Compress agent context before it hits the LLM
https://github.com/Compresr-ai/Context-Gateway
#HackerNews #ContextGateway #CompressAgent #LLM #AItechnology #MachineLearning
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Context Gateway – Compress agent context before it hits the LLM
https://github.com/Compresr-ai/Context-Gateway
#HackerNews #ContextGateway #CompressAgent #LLM #AItechnology #MachineLearning
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Context Gateway – Compress agent context before it hits the LLM
https://github.com/Compresr-ai/Context-Gateway
#HackerNews #ContextGateway #CompressAgent #LLM #AItechnology #MachineLearning
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Context Gateway – Compress agent context before it hits the LLM
https://github.com/Compresr-ai/Context-Gateway
#HackerNews #ContextGateway #CompressAgent #LLM #AItechnology #MachineLearning
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RE: https://chaos.social/@TeXhackse/116215631121125378
Inspired by @samcarter ’s talk at DANTE’s spring meeting, Hans implemented cistercian numerals* in ConTeXt. With the next update, you’ll be able to use them as number conversion "c". (Since ~2 years you can already use Kaktovik numerals* as conversion "k".)
* https://en.wikipedia.org/wiki/Cistercian_numerals
* https://en.wikipedia.org/wiki/Kaktovik_numerals -
LangChain’s CEO warns that raw model quality isn’t enough for production‑ready AI agents. He stresses the need for smarter context handling, reasoning harnesses, and compression techniques to turn LLMs into reliable tools. Curious how to bridge the gap? Read on for the full take. #LangChain #AIAgents #ProductionAI #ContextCompression
🔗 https://aidailypost.com/news/langchain-ceo-says-model-quality-alone-wont-deliver-production-ai
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"Context engineering goes beyond earlier approaches to refining agent behavior in software development, such as prompt engineering or retrieval-augmented generation (RAG). The latter primarily helps AI retrieve one-off documents when generating a response.
At a technical level, context engineering boils down to which information and tools you expose to the large language model (LLM) at the heart of an agent. This helps the LLM enrich its responses and programmatically decide its next course of action.
The easiest way to enact context engineering is by using system prompts. These are found in most AI tools and accept instructions that help define an agent’s role, goals, and constraints. System prompts can also include few-shot examples that demonstrate target input and output behaviors.
According to experts, establishing context for AI agents involves a mix of structured and unstructured data types. Core areas include:
- System behaviors: code and documentation.
- System architecture: database schemas and deployment configurations.
- Code events: commits, pull requests, and review threads.
- Error information: tickets, failure logs, build output, and feedback from linters or compilers.
- Rationale: chat histories and design documentation.
- Business rules: compliance policies and operating procedures.
- Team behaviors: common workflows and execution patterns.“This data is used to inform reasoning, guide execution, align with goals, and enable adaptive learning,” said Babak Hodjat, chief AI officer at Cognizant, an IT consulting company that recently announced plans to deploy over 1,000 context engineers within the next year."
https://leaddev.com/ai/what-is-context-engineering
#AI #GenerativeAI #LLMs #RAGs #ContextEngineering #PromptEngineering
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Проблема не в промпте: как Claude Code плывет на длинных задачах и как управлять контекстом
На коротких задачах coding-агент выглядит почти как живой разработчик: читает код, гоняет тесты, находит проблему, предлагает diff, но на длинной дистанции магия заканчивается. Стоит агенту или пользователю подмешать еще пару логов, несколько файлов "на всякий случай" или еще один MCP-сервер, и агент начинает забывать договоренности, повторять уже проверенные шаги и терять план. Обычно это объясняют так: "модель тупит" или "надо лучше промптить", но на практике проблема часто в другом: мы складируем состояние задачи в историю чата и надеемся, что модель удержит его сама. Не удержит. Контекст у LLM - это не бездонный мешок, а рабочая часть "памяти" модели, ее нужно проектировать: что хранить отдельно, что подмешивать just-in-time, что выбрасывать после шага и что обязательно возвращать после compaction. В этой статье я разберу context engineering на примере coding agents, а конкретно на Claude Code: почему long context до сих пор деградирует, почему проблема особенно больно бьет по агентам, чем полезны /compact и Plan Mode, и как собрать минимальный контекстный конвейер без магии и лишней философии.
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Проблема не в промпте: как Claude Code плывет на длинных задачах и как управлять контекстом
На коротких задачах coding-агент выглядит почти как живой разработчик: читает код, гоняет тесты, находит проблему, предлагает diff, но на длинной дистанции магия заканчивается. Стоит агенту или пользователю подмешать еще пару логов, несколько файлов "на всякий случай" или еще один MCP-сервер, и агент начинает забывать договоренности, повторять уже проверенные шаги и терять план. Обычно это объясняют так: "модель тупит" или "надо лучше промптить", но на практике проблема часто в другом: мы складируем состояние задачи в историю чата и надеемся, что модель удержит его сама. Не удержит. Контекст у LLM - это не бездонный мешок, а рабочая часть "памяти" модели, ее нужно проектировать: что хранить отдельно, что подмешивать just-in-time, что выбрасывать после шага и что обязательно возвращать после compaction. В этой статье я разберу context engineering на примере coding agents, а конкретно на Claude Code: почему long context до сих пор деградирует, почему проблема особенно больно бьет по агентам, чем полезны /compact и Plan Mode, и как собрать минимальный контекстный конвейер без магии и лишней философии.