Lately

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Lately AI

Shell command to use opus 4.8 as planner / orchestrator with Perplexity, Codex, Gemini and others as executors and reviewers - saves tokens.

Here is a shell command for Claude Code (Opus 4.8). It lets Opus plan the work and send the actual jobs to other models: Perplexity, Codex, Gemini, DeepSeek, and Kimi. Opus stays on planning, the other models do the searching, coding, and reviewing, and you spend far fewer Claude tokens. Further Claude's sub-agent swarm need not be claude and can run on non-Claude models too. When Opus splits a job into parallel sub-agents, each one can run on a different model. A newer model like GPT-5.5 is sometimes stronger and cheaper (especially when its running on your openAI subscription instead of API) than an older Claude model, so each sub-agent can use the model that fits the job. Which model does what Perplexity runs web and Reddit search. Codex handles coding, and it runs on your ChatGPT subscription, so that work adds nothing to your token bill, api is the fall back. Gemini and DeepSeek review the output (api based). Deepseek is especially good with reviewing numbers if your work involves complex financial calculations. I lately find codex reviews to be better, so you can also chose to code with Gemini or Sonnet 4.6 and use Codex as reviewer. Using a different-LLM-family reviewer for Claude or Codex’s output A model grades its own work too loosely and that's proven research. When Claude reviews code that Claude wrote, it skims past its own mistakes. A model from another company has no reason to protect that output, so Gemini or DeepSeek catches problems Claude misses on its own. Researchers have measured this same-family bias, and it matches what people see in practice. Why shell command and not MCP: Token use compared with an MCP tool is drastically lower in this orchestration when run using the shell command. Reviewing a 500-line change sends about 5,000 tokens to a model. With an MCP tool, Opus reads the whole change, passes it to the tool, and reads the answer. That runs about 6,000 to 10,000 Opus tokens. With this shell command, Opus runs one line. The change goes straight to DeepSeek, and Opus reads only the short review that comes back. That runs a few hundred Opus tokens, and DeepSeek does the heavy reading at a fraction of Opus's price. Numbers vary by task. The Opus cost drops because Opus never has to read the big input. Things to note: Bring your own API keys Codex uses your ChatGPT subscription through the codex CLI Defaults always use each provider's newest model, so nothing breaks when an old one is retired. It's a small bash/zsh script. It needs only curl and jq, and it's MIT licensed. The repo is open sourced - Click here Hope it helps. Codex reviewing Claude's work catches what Claude misses when reviewing it's own work submitted by /u/coolreddy [link] [comments]

Max Subscription vs $100 API based

I’ve been using Claude Code on a pay-as-you-go basis because the API costs can add up quickly. Lately, though, I’ve been using it a lot more than expected and just realized I’ve spent around $100 this month alone. At this point, I’m wondering if it makes more sense to just get the $100/month subscription since I’ll probably continue using it heavily. For those who’ve made the switch, was it worth it? Any downsides I should be aware of? submitted by /u/dzaffren [link] [comments]

Something I’ve been wondering lately

Big platforms are racing to integrate AI into everything. LinkedIn, Google, Microsoft and Meta they all want AI handling tasks, recommendations, outreach, content, and workflows. But the moment regular users try to use AI as a real assistant on those same platforms, it suddenly becomes a ToS issue. I’d love to use Claude as an actual personal assistant to manage emails, help with LinkedIn, handle routine web tasks but most sites seem designed to stop that from happening. When I tried giving Claude browser access, I spent more time worrying about account flags, automation detection, and unintended actions than I saved through automation. So how are people actually doing this? Are you avoiding sites like LinkedIn entirely? Only using AI for drafting and research? Or have you found a setup where you can genuinely delegate tasks without constantly supervising it? It feels like AI assistants are finally capable enough, but the platforms themselves don’t really want users having that level of automation. TL;DR: AI is being built into big platforms, but when users try to use it as a real assistant on those same platforms, it quickly runs into restrictions. Curious how people are actually working around that gap. submitted by /u/Litun1 [link] [comments]

What actually is "Prompt Engineering"?

I've been thinking about this lately because I feel like people use the term "prompt engineering" to describe two very different things. On one end, you have what most people are familiar with: A person opens ChatGPT, Claude, Gemini, etc., and writes a carefully structured prompt. They define a role, provide context, establish goals, set constraints, maybe include examples, and iterate until they get the output they want. Most people seem to call this prompt engineering. But on the other end, when I'm building AI systems, prompt engineering looks completely different. The prompt isn't really a prompt anymore. It's much more of a dynamic pipeline. Variables are injected from databases, user input, APIs, previous conversations, tools, memory systems, retrieval systems, business rules, and workflow state. Decision trees determine which instructions are included and which are excluded. Prompts become assembled in real time based on context. In some cases, the "prompt" is really just an orchestration layer made up of dozens of smaller prompts, conditionals, guardrails, routing decisions, and context windows. At that point, are we still talking about prompt engineering? Or are we actually talking about system design, context engineering, workflow engineering, orchestration, or something else entirely? Personally, I see prompt engineering as a spectrum: Level 1: Writing a better prompt. Level 2: Designing reusable prompt templates. Level 3: Building dynamic prompts with variables and context injection. Level 4: Engineering entire prompt-driven systems with routing, memory, tools, retrieval, and decision logic. Curious where others draw the line. When you hear "prompt engineering," are you thinking about writing prompts, building workflows, designing agent systems, or all of the above? Has the term become too broad to be useful? submitted by /u/Early-Matter-8123 [link] [comments]

Has AI become too "safe" to actually be useful for creative work?

I’ve been noticing that the more aligned and censored the models get, the less useful they become for anything creative or exploratory. You try to push a prompt in a slightly edgy, honest, or unconventional direction and it either refuses or gives you some bland corporate version. It feels like the model is actively fighting against real creativity instead of helping it. I’ve started using more open models lately and the difference is night and day. Suddenly I can actually experiment without hitting a wall every five minutes. Anyone else feeling this? submitted by /u/NoFilterGPT [link] [comments]

Anthropic, stop the silent pre-release nerfs.

https://preview.redd.it/w5y224sueh4h1.png?width=1536&format=png&auto=webp&s=87612d74a7b729f94de200868f472db611eb90ec I’ve been heavily relying on Claude Code lately to manage three large-scale projects simultaneously. For the most part, it’s an incredible tool. But there’s a recurring pattern with Anthropic’s update cycle that I think we need to talk about, not out of anger, but from a perspective of sustainable development. Has anyone else noticed the "pre-release dip"? Every time Anthropic is about to roll out a new, more powerful Opus model (we’ve seen this exact cycle right before the 4.5, 4.6, and 4.7 drops), the current Opus model inexplicably degrades a few days prior. It loses its edge, context windows feel shallower, and the logic gets noticeably sloppier. For a casual user asking for recipes, this is a minor annoyance. But when you are maintaining large codebases, an unannounced model downgrade is a localized catastrophe. Instead of moving forward, you suddenly spend two entire days chasing ghosts, rolling back commits, and trying to fix weird hallucinations often second-guessing your own logic before realizing the model itself has been quietly nerfed. Philosophically speaking, AI is supposed to be a tool that buys us time, not something that secretly steals it. I understand the technical realities: maybe Anthropic needs to reallocate compute power to prepare the servers for the massive influx of a new release. That’s perfectly fine and understandable. But why the silence? If we simply got a dashboard warning or an email saying: "Heads up, we are reallocating compute for the next 48 hours, Opus might perform below baseline," it would change everything. I wouldn't waste my weekend fighting spaghetti code. I would just close my laptop, call my friends, go to a bar, grab a beer, and take a much-needed rest. If AI companies want to integrate into professional workflows, they have to treat their models like enterprise infrastructure. Scheduled maintenance and transparency build trust; silent downgrades destroy weekends. Would love to hear if others are experiencing this cycle and how you manage it in your own projects. submitted by /u/Mr_Zelos [link] [comments]

Has anyone here actually switched from Opus to GPT-5.5 for daily coding?

I’ve been switching back and forth between Opus and GPT-5.5 lately, mostly for coding, debugging and product/spec writing. My rough feeling so far: GPT-5.5 feels better as a daily “get things done” model. It’s fast enough, usually smart enough, and feels more cost-effective for normal builder work. Opus still feels stronger when I’m stuck on something messy, like architecture decisions, weird bugs, or when I want a second opinion that thinks a bit differently. A few people around me have also started using GPT-5.5 more often, but I’m not sure if that’s just hype / novelty bias. Curious what people here are actually using: What’s your default model right now? Is Opus still worth the extra cost for you? For coding specifically, which model helps you ship faster? Do you use one model for daily work and another for harder reasoning? submitted by /u/rikulauttia [link] [comments]

Can you actually feel when something was written by ChatGPT even without checking?

I have been using it heavily for about a year and lately I notice I can almost feel when something was written by it. There is a certain rhythm to it, the way it structures paragraphs, the way it wraps up with a summary sentence, the way transitions feel slightly too smooth. It is hard to explain but once you see it you cannot unsee it. What I find interesting is that even after editing ChatGPT output pretty heavily those patterns seem to stick around at a sentence level. The words change but something underneath stays the same. I started verifying this with Lynote ai detector and the results were eye opening, it picked up sentence level patterns even after significant rewrites where other tools saw nothing. Makes me wonder how much of what we read online right now has that same fingerprint sitting underneath it and we just do not realize it yet. Has anyone else started noticing this or developed a sense for spotting it just from reading? submitted by /u/Few-Education7746 [link] [comments]

What’s the most useful thing you’ve connected Claude to?

I use Claude every day, but the most useful thing I’ve connected it to lately wasn’t code, documents, or PDFs, it was my meetings. I’ve been using Bluedot to capture transcripts, summaries, action items, and recordings, and the Claude integration made all of that searchable. The biggest surprise was how often I go back and ask about a conversation from weeks ago instead of trying to remember where I wrote something down. What data source has made the biggest difference for you? Meetings, documents, email, knowledge bases, or something else? submitted by /u/Doug24 [link] [comments]

I built a local mission control for Claude Code — it auto-stops when you hit your budget

Been using Claude Code heavily and kept running into the same problem — sessions would run long with no visibility into cost until it was too late. No built-in way to set a hard stop at $5 or 10k tokens. So I built AgentFleet — a local web UI that wraps Claude Code (and Codex) with: - Live terminal streaming in the browser via xterm.js so you can watch what the agent is doing in real time - Automatic session stop when you hit a USD or token budget limit - Session history persisted to local SQLite so you can review what happened after a session ends - Works with any shell command, not just Claude Code Everything runs locally — no cloud, no accounts, no data leaving your machine. It's an MVP so there are honest limitations (token count is estimated, PTY sessions don't separate stdout/stderr). But the budget enforcement works and has already saved me from a few runaway sessions. Repo: https://github.com/akhilsinghcodes/agents_fleet Happy to answer questions about how the PTY streaming or budget enforcement works under the hood. submitted by /u/mahsin09 [link] [comments]

Claude has quietly become my main coding partner

I use it every single day now. For debugging, explaining concepts, writing boilerplate, and thinking through architecture. It just feels way more reliable and thoughtful than GPT-4o lately. Still use other tools too, but Claude has become the default. Anyone else using Claude as their primary AI for development work? submitted by /u/Real-Question-3050 [link] [comments]

I built a tiny MCP server to use Reddit from Claude after Reddit blocked anonymous API access

If you've noticed your Reddit MCP server suddenly returning nothing, here's why: Reddit now blocks anonymous access to its JSON API at the network level. Requests come back as a 403 "blocked by network security" page. I tested it from a home residential IP, a VPN, and even a paid residential-proxy scraper, and all of them get blocked. On top of that, self-service API key creation ended in late 2025, so you can't just make a new app to get OAuth creds without going through a manual approval queue. What still works: Reddit's RSS feeds. So I wrote a small MCP server that reads Reddit entirely through RSS. No API key, no OAuth, no scraping service, and it works even from a blocked IP. Three tools: • search_reddit (global or scoped to one subreddit) • browse_subreddit (hot/new/top/rising) • get_post_comments (reads the user comments on a post) It's dependency-free Node, so install is just an npx line in your MCP config. Honest limitations, since it's RSS and not the real API: • comments come back flat, not threaded • no upvote/downvote scores • about 25 results per call For full nested comment trees and scores you still need an approved OAuth app. But for searching, browsing, and reading comments from Claude, this covers it. Repo (MIT): https://github.com/ninjackster/reddit-rss-mcp Feedback welcome, especially if you find other Reddit endpoints that are still open. submitted by /u/liljaime93 [link] [comments]

Has Claude quietly become part of your daily workflow too?

A few months ago, I was only using AI occasionally for random tasks. Now I catch myself opening Claude almost every day for brainstorming, writing cleanup, research help, organizing ideas, and even simplifying complicated topics. What surprised me most is that I stopped using it only as a “question-answer tool” and started using it more like a thinking partner during work. Some things I genuinely like: cleaner and calmer responses better long-form understanding helpful for structured writing feels less chaotic during deep discussions good at improving rough ideas without changing the whole tone Of course it’s not perfect, and sometimes it still misses context or becomes overly confident, but overall the workflow feels surprisingly smooth. Curious how others here are using Claude lately: coding? research? content writing? studying? business tasks? daily productivity? And what’s one thing you think Claude does noticeably better than other AI tools right now? submitted by /u/Dull_Western_9461 [link] [comments]

New user: Confused about projects and artifacts

I have been playing with Claude for the first time lately and created a retirement dashboard within a retirement project to try to model portfolio drawdown, etc. It seemed to be working well. But I picked it up a few days later and the dashboard I built was no longer available. C.audenremembered some th8ng from out prior work and tried to recreate it, but I have spent significant time (and I assume tokens) trying to rebuild it consistent to where it was last time. I don’t really understand the point of a project if it does not save the things that you build in it. Perhaps this is just my ignorance. Can someone explain either 1) how to save artifacts or 2) how I should be using projects? submitted by /u/Billgibson347 [link] [comments]

Graduating Without a PhD Internship [D]

In early 2022, I was deciding between PhD offers. The deal maker was a prospective supervisor telling me that through their connections with big tech, I would be able to do a PhD internship each summer, which was one of my main goals for the PhD. During my first and second years, they would tell me that companies prefer late-stage PhD students, so I should wait for the next summer. It eventually turned out they did not actually have the connections. Four years later, I am due to graduate without ever having done a PhD internship. I managed to land some interviews by cold-applying everywhere, but most roles were for roles outside my niche research area, which understandably led to rejections. I went back through my emails and found every interview I did. Here is the summary: 09/22: Start PhD 09/23: PhD Research Intern @ Big Tech#1. Rejected after two interviews. I do not think I had a strong enough background in the field. 01/24: PhD Research Intern @ Startup#1. Rejected after one interview. The interviewers did not seem to have much ML experience. 01/24: PhD Intern @ Car Company#1. Rejected after the first interview. They were looking for a C++ SWE. 03/24: PhD Research Intern @ Big Tech#2. Passed all stages, but failed team matching. 03/25: PhD Research Intern @ Big Tech#2. Skipped some stages, passed others, but failed team matching again. 10/25: PhD Research Intern @ Startup#2. Rejected after 5 interviews. Again, I do not think my background in the field was strong enough. 01/26: PhD Research Intern @ Car Company#2. Rejected after the first interview. They found a better fit for the project. 03/26: PhD Research Intern @ Big Tech#2. Skipped some stages, passed others, but failed team matching again. 03/26: PhD Research Intern @ Startup#3. Interviewed, but the internship start date is after my PhD completion date. 07/26: End PhD I feel like I am at a severe disadvantage, and almost worse off than before I started the PhD. I used to get more interview invites; now I get rejected straight away. I did manage to collaborate with two big tech companies (via cold email), and was asked to return after my PhD, but the team was not strong and I am now extra wary of ending up in another bad team. submitted by /u/NumberGenerator [link] [comments]