Get Shit Done: A Meta-Prompting, Context Engineering and Spec-Driven Dev System

https://github.com/gsd-build/get-shit-done

#HackerNews #GetShitDone #MetaPrompting #ContextEngineering #SpecDriven #Development

Погоди переезжать на дешёвую модель: считаем effective cost с учётом кэша

В сообществе часто пишут о том что KV-cache hit rate - один из главных приоритетов при оптимизации стоимости. Manus в той-самой статье про context engineering и своего агента, TikTok в кейсе про оптимизацию AI-агента для тестирования, AiSDR в статье про перестройку шаблонов генерации писем. Стало интересно стало: а как это считается у MaaS-провайдеров? Я знаком со скидкой за кэш токены, но никогда не садился детально разбирать экономику кэша у OpenAI, Anthropic, Gemini, DeepSeek - с цифрами, сценариями, сравнением. Сел считать. Первый же результат удивил: два запроса к одной и той же модели с одним и тем же объёмом токенов - разница в цене в 3 раза. Зависит только от того, попали ли токены в кэш. Про то как KV-кэш устроен внутри - уже хорошо написано на Хабре, ссылки в конце. Здесь только про деньги и токены: как считать реальную стоимость и почему прайс-лист для этого не подходит только прайс-лист.

https://habr.com/ru/companies/bitrix/articles/1008320/

#ai #нейросети #contextengineering #promptengineering #openai #anthropic #gemini #tokens #prefix_caching

"The new lifecycle is tighter loop
The SDLC was a wide loop. Requirements → Design → Code → Test → Review → Deploy → Monitor. Linear. Sequential. Full of handoffs and waiting.

The new lifecycle is a tight loop.

Intent. Build. Observe. Repeat.

No tickets. No sprints. No story points. No PRs sitting in a queue. No separate QA phase. No release trains.

Just a human with intent and an agent that executes.

So what is left?
Context. That’s it.

The quality of what you build with agents is directly proportional to the quality of context you give them. Not the process. Not the ceremony. The context.

The SDLC is dead. The new skill is context engineering. The new safety net is observability.

And most of the industry is still configuring Datadog dashboards no one looks at."

https://boristane.com/blog/the-software-development-lifecycle-is-dead/

#AI #GenerativeAI #AIAgents #SDLC #SoftwareDevelopment #Programming #ContextEngineering

🧠 #PromptEngineering isn't dead — it evolved. Prompt being in the job title may be fading, but the skill is foundational. Real #AI success isn't just about better prompts. It's about building systems with 5 critical layers: prompting, context, memory, #signals & intent. 🤖⚙️ The orgs winning right now think in systems — not just tasks. Read more 👇

#ArtificialIntelligence #ContextEngineering #IntentEngineering
https://stemsearchgroup.com/prompt-engineering-is-not-dead-it-just-found-its-place/

New post: WTF is Context Engineering?

Not just better prompts. The full information architecture your LLM sees at runtime: memory, retrieved docs, tool results, history, and how it's all ordered.

Also: why infinite context windows don't fix this.

https://open.substack.com/pub/takschdube/p/wtf-is-context-engineering?r=6dlywb&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

#AI #LLM #ContextEngineering

"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

Rohan Paul (@rohanpaul_ai)

논문은 AI 컨텍스트 관리를 파일 시스템처럼 다루는 것이 최선이라고 제안합니다. 현재 지식은 프롬프트, 데이터베이스, 도구, 로그 등으로 분산되어 있어 컨텍스트 엔지니어링이 이를 일관된 체계로 통합해야 한다고 보고, 이를 위해 에이전트형(agentic) 파일 시스템을 제안합니다.

https://x.com/rohanpaul_ai/status/2028184543040270769

#contextengineering #aimemory #agenticsystems #research

Tejas Kumar (@tejask)

작성자는 @cursor_ai가 지금까지 본 컨텍스트 엔지니어링(문맥 처리) 구현 중에서 실행력이 가장 뛰어나다고 칭찬하며, 그 우수한 실행력을 인정함. 도구 측면에서 컨텍스트 관리 및 설계가 특히 잘 되어 있다는 평가.

https://x.com/tejask/status/2028093709171241276

#cursor #contextengineering #aitools #developertools