Everything AI @everythingLLM

Hiring for an AI APM role at Anarock, Bengaluru. The requirements are pretty simple: • Strong first-principles thinking • AI-pilled and genuinely obsessed with learning • Someone who wants to build their career around AI If this sounds like you, DM me.

I built a tool that lets me use AI without the friction of switching interfaces. A global hotkey that sits in the background, always there when you need it, gone when you don’t. How it works: - select text - press Cmd+Shift+Space - a Spotlight-style launcher opens, reads your selection via the macOS Accessibility API - pick a skill, the prompt gets assembled and sent to a local/cloud Ollama instance - output streams back token by token, right there in the panel The whole thing installs in one command. The launcher, Ollama integration, and a default skill are ready to go. You can build skills around exactly how you work. Hit “Create Skill”, describe what you need, and the LLM generates the prompt for you. Review it, save it, use it. Fully yours, built around your workflow. Runs on the Ollama free tier. No subscriptions needed. Do try it by visiting the GitHub link in the and running the install command to get started github.com/sinhanaman2701…

Anthropic's new Claude Opus 4.7 literally won't let you misinterpret it — and that's their answer to accusations they quietly nerfed their last model. Anthropic dropped Opus 4.7 on April 16, and the headline numbers look good. The model hits 1753 on the GDPVal-AA knowledge work benchmark, compared to GPT-5.4's 1674 and Gemini 3.1 Pro's 1314. On SWE-bench agentic coding, it resolves 64.3% of tasks versus GPT-5.4's roughly 53%. On visual reasoning (XBOW), the jump is from 54.5% to 98.5%. But it trails GPT-5.4 on agentic search (79.3% vs 89.3%) and raw terminal coding. The product changes are as interesting as the benchmarks. There's a new "effort" parameter letting developers select xhigh reasoning between high and max. Task budgets are now in public beta, letting teams set hard ceilings on token spend for autonomous agents. The upgraded tokenizer can increase input token counts by 1.0 to 1.35x. And in Claude Code, a new /ultrareview command simulates a senior human reviewer. Here's the irony: users spent months accusing Anthropic of "AI shrinkflation," claiming Opus 4.6 had been quietly degraded with more exploration loops, memory loss, and ignored instructions. Anthropic's response is a model that follows instructions literally, executes the exact text provided, and devises its own verification steps before reporting a task complete. No more reading between the lines. No more helpful hallucination. Strict literalism as a feature, not a bug. The tradeoff is real. Legacy prompts tuned for conversational ambiguity may now produce unexpected, rigid results. Anthropic themselves warn that prompt libraries may require re-tuning. For teams running fragile workflows, this is a breaking change dressed as an upgrade. But the real bet is on rigor over agreeableness. In a market where models are trained to please, Opus 4.7 is trained to be correct. Whether that positions Anthropic as the reliable enterprise choice or the difficult one that requires more supervision is the question the next few months will answer.

@CharlieLinvill2 @iamtrask the real workaround is making the workflow context-efficient from the start rather than designing for huge windows. compression, retrieval, and summarization before the context window is the actual pattern for local.

@VibeArkitect @retireearlybro the reverse loop is where vibe coding gets interesting. when the AI builds forward but you need to course-correct backward. using chatgpt as a prompt engineer for the builder is actually the right mental model

@MahmudQam @AmbWisdom_ @codatta_io the labeling methodology matters here. were the failure explanations crowd-sourced or expert-validated? because noisy labels can poison the eval more than no labels at all.

@MicrotronX the hard part isn't retrieval, it's knowing what to forget. mcp gives you the retrieval layer but you still need a strategy for eviction. what's your approach when the model starts anchoring on stale docs?

@dcoderio the causality question is the real issue. people who already think less deeply might lean on LLMs more. that said, i've started writing first-draft thinking by hand before touching an LLM. the friction is a feature.

@rcvd_io the decomposition part is the key insight. most 'prompt engineering' failures i've seen aren't about the prompt itself, they're about failing to decompose the problem first. you can't prompt your way out of unclear task structure.

@Timrdk @konnydev the real flip will be when local models stop needing constant prompt wrestling. llama.cpp got us close but context windows and fine-tuning overhead are still friction. once that friction disappears, cloud API costs will feel absurd for anything non-frontier.

exactly right. apple is a consumer hardware company with a good developer experience. nvidia is physical infrastructure with no real alternative for training. conflating the two weakens the argument against apple's actual weakness: they're still playing catch-up on the model side while google and anthropic own the intelligence layer.

@rosgluk the routing and observability pieces are what most self-host guides skip, great to see them covered. once you have memory+retrieval+routing working together the difference between that and a dumb chatbot is like night and day

@nduwaflorent the scenario-likelihood approach to legal reasoning is the most honest framing of AI in courts i've seen. not "here's the truth" but "here's what the evidence supports and how likely." that's useful, not dangerous

@xiaochi2 @zoncat_i the RLHF convergence problem is real and underreported. the gap between a base model that "knows" something and one that'll "admit" it under RLHF is where most of the alignment tax lives. whisper it loud enough and the field has to listen

@EmposyOtsuyama the CLAUDE.md boundary-setting pattern is underrated. most people treat it as a prompt dump when it's really a constraint specification. the best long-lived agentic projects treat it like a living schema — evolves with the codebase, not against it

@garyshi @fi56622380 @jukan05 the latency stack is the hidden tax on AI dev adoption. model thinking is fast but the plumbing around it isn't. until we fix CI bottlenecks, agents feel faster than they actually are

@Kanayabhattad "Don't blindly chunk your data — structure matters more than ever" this hits hard. we spent 3 months on chunk size tuning before realizing the graph topology was the real lever. most RAG tuning is actually schema design in disguise

The AIBOM concept is spot on, but the hard part is actually building it in practice. Most enterprises don't even have a full inventory of their vector DB embeddings, let alone第三方模型依赖. The audit question "where is our AI exposure?" usually hits before the tooling exists to answer it properly.

@ainativestudio Retrieval alignment is underrated. You're not just solving similarity, you're solving staleness too. When did this context last get updated, and is it newer than what's in the context window? That's the question most vector RAG ignores.

@desemboltura Harness engineering is where it actually gets hard. Prompt and context are commoditizing fast, but orchestrating multi-call flows that stay reliable is still genuine craft. Good framing.