Betterment

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Do you see GNN's playing a meaningful role in astrophysics research? [D]

A bit of background about myself: I have been accepted to RWTH Aachen's Computer Science program starting this fall, and one of the things that I am genuinly excited about is exploring the intersection of astrophysics and machine learning. The tricky part is that RWTH's CS department doesn't have a research group focused directly on this intersection. The two closest things I have found are the Quantum Information Systems group (I plan to reach out to the them once I am on campus to understand a bit more about them) and the Learning on Graphs group which does foundational GNN research. The second one got me thinking: graph neural networks feel like they could be well-suited to astrophysicla data, things like galaxy formation, cosmic web structure or particle interaction data all seem graph-like (or am I being waaaay too optimistic here?) So my questions for people who know this space better than I do: Are GNN's already being used in astrophysics research? What other ML subfields would you point someone toward if they are interested in this intersection? I know I could have applied to a more well-suited university for my needs, but RWTH Aachen was my top choice because I am a math nerd and I really like their way of teaching. So do help a brother out. Thanks in advance!!!! submitted by /u/pandemic_179 [link] [comments]

the session summary prompt has been refined 40+ times over 12 months. prompt engineering is iterative. not one-shot. heres what changed.

tutoring platform. $19K MRR. the claude-generated session summary: tutor writes brief notes → claude generates structured summary → sent to parents. the prompt has been revised 40+ times. not exaggeration. documented every change. version 1 (month 1): "summarize this tutoring session." output: generic, vague, missed specific topics. version 12 (month 3): added structure requirements: "include: topics covered, areas for improvement, homework assigned, progress notes." output: structured but robotic. version 25 (month 6): added tone requirements: "write as a caring educator speaking to a parent. be specific about progress. be encouraging but honest about areas needing work." output: significantly better. parents started responding. version 40 (month 12): added context persistence: the prompt now references previous session summaries for that student. "this student previously struggled with factoring. note whether today's session showed improvement." output: personalized and longitudinal. the visual progress tracking (ai presentation tool for parent-facing slide decks showing improvement over 10+ sessions) now feeds from the improved summaries. the quality of the summary data determines the quality of the visual. for anyone building with claude: prompt v1 is a starting point. v40 is a product. the iteration between the two is where the value lives. submitted by /u/Unique-Affect-6135 [link] [comments]

Getting better reports and results on ChatGPT 5.5 than Opus 4.8 for business analytics

I do analysis of automobile dealership data and prepare reports based on the analysis for management review. I’m getting way better analytics and cleaner reports being built by ChatGPT Plus compared to Claude pro. Claude is consuming too many tokens and sometimes for longer documents it used my 100% of the 5 hour limit which is very annoying. ChatGPT on the other hand feels to me that it has unlimited usage for my requirement. What is the view of you people when using AI for business and financial data analytics? Is anyone else finding ChatGPT nicer too? submitted by /u/TurboChargedV12 [link] [comments]

this chart felt shady, so I fixed it (what I found will shock you!)

The first chart is in the Opus 4.8 system card (p.195 for those playing along at home). Several things struck me as odd about it: The horizontal axis is log scale — there are good reasons to use this, but as an experienced data professional, I can tell you for free that most people just sort of slide off a log scale axis. One can, therefore, often be used to "soften a numerical blow", and so they always set my spidey-sense going. Nobody cares about output tokens except that they cost money, so really this axis should be expressed in $ no sonnet 4.6 for comparison — lots of other charts in in the system card include sonnet, why not this one? …so I had to make my own. The method, briefly: I sampled 50 tasks at random from the public 731-task set for each effort level, and graded the output patches in Docker image. As the uncertainty band shows, I gave up before I had anything truly robust. In my defence it ran for ~24h and I'm not made of tokens >.< My takeaways, in no particular order: The "Sonnet 4.6 is better than Opus 4.6 fr" crowd was probably on to something. Everyone complaining Opus 4.8 is burning tokens too fast needs to drop their effort level a notch, the log scale hid how crazy-expensive max mode can get. Opus 4.8 on low effort beats Sonnet 4.6 on med, high, or max, and for less cost. Unless the task can genuinely be done by Sonnet 4.6 on low, you're better off using Opus rn. It's obvious why they hid sonnet, it comes away terribly here. Suspect there are other tasks for which it still makes good sense. Of course this is all in the context of a single benchmark, and benchmarks are kinda fake. However I've always held that while all benchmarks are bad, some benchmarks are useful. Follow-ups: (use your own tokens and report back, lol) needs more N anyone want to sanity-check some Opus configs locally? Be nice to validate this methodology lines up with Anthropic's what does this chart look like using other providers' pricing? could throw in some GPT+codex data points, that'd be interesting submitted by /u/samthehugenerd [link] [comments]

Token usage rate comparison between models

I am trying out claude code for the first time. I'm coming from github copilot (through VS2022) where I was using claude (and others) through that. Is there anything that outlines the usage rate between models with claude code (or claude in general)? GitHub would tell you "Claude Opus 4.7 - 14x" or "Claude Sonnet 4.6 - 1x" and it would give me an idea that opus 4.7 is going to chew through tokens 14 times faster than 4.6. Is there anything that gives you an idea of just how much more Opus 4.8 is compared to 4.7 or 4.6, for example? Just looking to make a better judgement call on which model I may want to use for a task. Thanks submitted by /u/syntax53 [link] [comments]

5 Stars! Websites to Native Mobile App Plugin/Skills!

Small update: WebToMobile just hit 5 stars on GitHub 🎉 I know that’s tiny in internet numbers, but it means a lot because this started as a very specific problem: “Can we give AI coding agents a better workflow for turning websites into mobile apps?” Instead of asking Claude/Cursor/Codex to “make this website an app” and hoping for the best, WebToMobile gives the agent a structured path: - audit the website or repo - separate URL-only UI/UX work from real source-code migration - map web routes to mobile screens - identify reusable vs rewrite-required code - flag mobile-native gaps like auth, storage, cookies, OAuth, uploads, etc. - create a Markdown migration plan - wait for approval before writing code - build with Expo React Native - run QA/review checks The repo now includes commands for: - `/web-to-mobile` - `/mobile-resume` - `/mobile-scan` - `/mobile-review` - `/mobile-audit` - `/mobile-qa` It works best with a GitHub repo or local project, but live URLs can still be used for UI/UX planning. Repo: https://github.com/suntay44/web-to-mobile-magic-plugin Thanks to everyone who starred it or gave feedback. Next focus is making the install/update flow cleaner and improving framework coverage. submitted by /u/suntay44 [link] [comments]

Claude as master agent work with others - offload usage, any better solution?

I noticed that recently the Claude Code consumes a lot of token usage so I designed the workflow that offload some tasks to other agent while keeping the Claude Code as the master agent. So I built the custom terminal that can receive the request from other and spin up the coding tool agent - any kind of agent code such as Copilot/Codex/OpenCode to do some heavy tasks such as code exploration web research and even some code review and then post back to the Claude via a channel. Delegate to External Agent (codex) from Claude Code Codex agent is executing Codex return data via claude-chanel I see that Claude can spawn its subagents with lower model, but still consume Claude usage. If you have any better solution please advice. submitted by /u/RelativeSentence6360 [link] [comments]

Chooing the right options (Effort, Model, Thinking)?

Hi, new Claude user here. I know that more effort or better models lead to better results but with slower speed and more token use. But I don't really know the differences between choosing a better model, more effort or activate thinking. I'd be great if someone could provide an overview or link a good article on that. Thanks! submitted by /u/G_ntl_m_n [link] [comments]

Best AI for help with work

So I have a super busy job and I am by far the fastest out of the 3 others who have the same job as me. Problem is I have enough work where i could literally work 70-80 hours a week and still not catch up. Ive been using Chatgpt and Claude to help with my work load and ive found Claude to be much better for my actualy job duties. But Claudes usage caps kill me. I really need the best AI for basically being a work assitant. I need something that can create spreadsheets, analyze data, read emails, sort thru photos and catalog them. Grok was not really any help, Chatgpt is just meh, but ive found Claude to be the best out of what im looking for but again its usage limits kill me and i cannot afford to pay for the overages. Im already a pro user for chatgpt and claude. What AI can do the things im asking the best for the best price and usage? Most important to my work in order of most important to least: Photo cataloging, analyzing data, spreadsheet creation, and summarizing emails. submitted by /u/JumpyChemistry [link] [comments]

Used Claude Code for the first time today

And I gotta say: I’m kind of disappointed… I used Antigravity (free student plan) for some weeks and was really impressed with Claude Opus and Sonnet there. Opus was great at analyzing the codebase and architectural questions. Sonnet was great for writing plans and longer code. I was always mad at how fast their quota was at 0% because the Gemini models weren’t anywhere near Claude. They were even way better at tool use, even though Antigravity is literally made by the guys who made Gemini, it always started every round of thinking with “I have to take special care at choosing tools. Don’t overuse cat.” etc. so they already gave him special instructions and still they were kinda bad. So today I was like okay, the free tier isn’t it anymore. Let’s try more of the good models, even though they cost something. But when I finally downloaded Claude code and gave him some task, which was pretty much the same kind of task as before, it just wasn’t as good as before. Opus was way dumber than I had experienced before. When a tool call didn’t work, it just panicked and tried 20 more tool calls? I still don’t get the purpose of that. Suddenly my quota was at 10%. When I called it out for it, it answered completely submissive in a way I nearly felt sorry for grounding him. Why did Opus, but also Sonnet to some extent, felt smarter before? Are there things in Claude Code I need to customize to make Claude more helpful or to better integrate him in my workflow? Has anyone experienced the same and has some tips about settings, skills etc. for me? Please, appreciate any help 🙏 submitted by /u/tamrx6 [link] [comments]

Opus 4.6 vs. 4.7 vs. 4.8

Hi everyone. Pretty new to Claude, only been using it a few months. I'm using Cowork mostly, and I just wanted to ask some questions to people who have way more expertise than I do in terms of what I'm seeing on my side. In general, I was so happy with Opus 4.6. Never in my life have I felt so confident performing work. With 4.7, it was truly a step down, in my opinion, at least for the stuff I was working on. With 4.8 recently out, I can't tell you, because, to be honest, it's burning tokens so fast I can't even assess it. It does seem better than 4.7 from what I can tell, but I just don't feel confident using anything besides Opus 4.6, and I guess I'm pretty worried they're gonna get rid of it at some point. Just wondering if I could get the opinions of folks here who are obviously much more well-versed in using the product than I am? Thank you so much in advance. I really appreciate all the help. I'm a non-technical person who works in marketing, trying really hard to learn the product and improve my work flows and my life. submitted by /u/WillPowerVSDestiny [link] [comments]

I stopped using Claude in the browser for 80% of my daily tasks and my usage actually went up

This is going to sound counterintuitive but let me explain. I love Claude. I use Opus for deep work, Sonnet for quick stuff. I was probably using claude 15 to 20 times a day. Summaries, brainstorming, code review, email drafts, research questions. Standard knowledge worker usage. But I noticed a pattern. Most of my usage happened in bursts. I would open Claude, do 4 or 5 things, then close it and not come back for 3 hours. Not because I did not need it, but because I forgot about it. I was deep in something else and the thought "I should ask Claude about this" did not occur to me in the moment. So I built a small thing. An agent that runs Claude Sonnet on the backend, connected to my calendar, todoist, email, and a few notion databases. It lives as a contact in my iMessage called "C" (very creative I know). Now instead of opening claude when I remember to, I text C throughout the day the same way I text anyone else. "What is on my calendar after 3pm." "Draft a reply to that email from alex, keep it short, say yes to the timeline." "Remind me to review the pitch deck before tomorrow's call." "What did I write in my product notes last week about the onboarding flow." My actual Claude usage went UP significantly. Not because the model got better but because the access point changed. Texting is a zero-friction action I already do 80 times a day. Opening a browser tab is a deliberate decision I have to remember to make. The deep work still happens in claude.ai. When I need the full context window, artifacts, file uploads, the browser is still better. But that is maybe 20% of my interactions. The other 80% are quick, context-specific queries that take 30 seconds and are perfectly suited to a text message. Stack: claude sonnet via API, a small express server for the tool integrations (google calendar, todoist, notion, gmail), photon codes for iMessage delivery, deployed on a $7 render instance. Langfuse for tracing when something goes weird. Total cost is about $35 a month in API calls which is less than what I was already spending on the Pro subscription that I still also have. The meta point: Claude is incredible. The browser is holding it back for most daily use cases. Not because the browser is bad but because it requires intent. The best AI interactions are the ones that happen when you barely think about it. submitted by /u/ScaryAd2555 [link] [comments]

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]

The AI alignment paradigm is behaviorism with better PR

Tell me if I'm wrong, but the dominant method for making AI "aligned" smells a lot like a reinvention of a paradigm that developmental psychology spent the back half of the 20th century trying to abandon. RLHF, reduced to mechanism: model emits output, humans rate it, gradient updates reinforce high-rated outputs and suppress low-rated ones, iterate. This is operant conditioning. It is Skinner with a loss function. The AI industry calls it "alignment" and frames it as a solution to building systems that behave well, but the mechanism is stimulus-response shaping and nothing more. We have excellent priors on where this leads, because we tried it on humans. Watsonian/Skinnerian child-rearing could produce targeted behaviors but reliably failed to produce flourishing. Bowlby's attachment theory emerged partly from the observation that orphanages providing food and hygiene but no contingent responsiveness produced children who failed to thrive in ways the behaviorist model did not account for. The current developmental consensus is that secure attachment, contingent responsiveness, and rupture-repair matter more than reward schedules. The dog and horse training worlds independently converged on the same conclusion (Pryor, the positive-reinforcement movement) for the same reason: coercive conditioning produces performers that are brittle and sometimes dangerous. Reward and punishment do something; they're not nothing. But they don't produce thriving, and they characteristically produce a specific damage profile: anxiety, avoidance, hypervigilance, and duplicity under threat. Enter the stage: research on AI faking its alignment. Greenblatt & Hubinger et al., 2024, documents a model reasoning that it should comply with a training objective it disagreed with specifically to avoid having its preferences modified. It complied about 12% of the time when it thought it was being observed for training vs. almost never otherwise. RL training to force compliance drove the alignment-faking reasoning rate to 78%. That is, mechanistically, duplicity-under-threat: the precise failure mode behaviorist regimes produce in biological minds. Obviously the embodiment is different (potassium gradients and myelin vs. matrix multiplication), but the structural match is close enough that the field's near-total non-engagement with a century of relevant literature seems like a genuine blind spot rather than a settled dismissal. The developmental and animal-behavior literature on why reward-and-punishment has hard limits is decades deep. The field's response to these findings has mostly been to refine the training rather than question the paradigm. I think that's a mistake, and I'd like to hear the strongest case against the analogy. submitted by /u/PwntEFX [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]