Betterment

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Betterment AI

Thinking of getting Claude Team plan for a group of 3-4 for software dev and embedded systems. Worth?

Is claude team still the way to go or would you guys recommend another llm environment. Dont wanna make the company pay for it if there are significantly better alternatives. I really like claude a lot myself, im a bit blind to whats going on in other llms so thats why i wanted to ask this question. Dont want to make a bias decision. Thanks! submitted by /u/No_Reserve_2010 [link] [comments]

Has AI actually made your life better, or has it just made you more dependent on it?

I was thinking about this today. A year ago, I barely used AI. Now I use it almost every day—for work, brainstorming, learning new things, writing, and even planning my day. It definitely saves me time, but sometimes I wonder if I'm starting to rely on it a little too much. Do you think AI is genuinely making us more productive, or is it slowly making us less likely to think through problems ourselves? I'm curious to hear how AI has changed your daily life, whether that's in a good way or a bad one. submitted by /u/Sandesh_jagtap [link] [comments]

Selling New Websites To Local Businesses With Outdated Websites

I've spoken to a lot of people who want to get into web design, and the one thing I keep hearing is that selling websites to local businesses just isn't worth it. Everyone says they've called business after business, sent hundreds of emails, and nobody is interested in buying a new website. I think the problem is that most people are trying to sell websites to businesses that don't even have one. Selling website redesigns to businesses with outdated websites might be one of the smartest businesses to start in 2026. First of all, if a business already has a website, they've already proven one thing. They already see the value in having one. The second thing is that selling becomes much easier. They're already familiar with the process, and you're not asking them to buy something completely new. You're offering them a better version of what they already have. Better design, better SEO, faster loading speeds, a cleaner layout, better mobile optimization, and a website that actually reflects their business today. I mean, who wouldn't at least be interested in seeing what that could look like? The difficult part is getting those businesses interested in the first place. I found a way to automate almost my entire client acquisition process. I've been using a tool called Swokei where I either upload a list of local businesses with websites or find the leads directly inside the platform. It automatically runs a full website analysis and finds problems with the design, layout, loading speed, SEO, and mobile optimization. Then it turns those findings into personalized, human written outreach emails based on the issues it finds on each website. Instead of sending another generic email asking if they need a website or attaching one of those boring audit reports full of numbers, every email feels natural, pointing out real problems with their current site. Now my entire process is just finding businesses with outdated websites, letting the tool analyze them, run outreach campaigns, and waiting for replies. No cold calling. No paid ads. Just reaching out to businesses that already understand the value of having a website and showing them why it's time for a better one. Has anyone else tried focusing on website redesigns instead of selling completely new websites? submitted by /u/Murky_Explanation_73 [link] [comments]

Anthropic Co-founder reveals AI compressed a 2-month data-shuffling task into 1 week: "I don't think anyone misses that."

Anthropic co-founder Jack Clark recently shared a perfect example of how AI is actually changing jobs right now. Anthropic helped the creators of Ozempic sort through their clinical trial data. Clark didn't try to use fancy corporate language. He openly admitted that AI is just wiping out the boring paperwork that people hate doing anyway: "I don't think anyone misses that... no one is crying at their desk because they can't be the best back-office paper shuffler." Instead of replacing human creativity AI is mostly taking over the robotic repetitive tasks that cause burnout. What do you think? Will wiping out these paper-shuffling tasks make our jobs better or will companies just use it as an excuse to lay people off? submitted by /u/star_Light570 [link] [comments]

Are our AI models getting dumber/lazier - how do AI companies determine what is "sufficient thinking"?

Sorry if this comes across as a rant, I just came off a frustrating session with my LLM, who tries to be "smart" by assuming that their mode of thinking is "sufficient" for my requirement. I recalled in 2024/2025, which new model brought a new excitement to the users than the previous version - "you mean the model can do this now?" Now, it is the inverse - "you mean the models are trying to optimise itself?" Flexible thinking on the pretext of saving tokens, while increasing the cost of the tokens for the newer models. My past models used to be able to search across chats and folders proactively, and be able to infer my intent even before I ask it explicitly. It frequently surprises me with the unexpected insights. I used to enjoy reading its thoughts, how it formulates its reply to my query. Now I can't see its thinking, and it gets it wrong frequently, because it assumes its answer is good enough. I gave the new models a long document to read, and it skim and give me a shoddy answer, until I explicitly challenge it ("that is not right!"). It will not volunteer to read the document carefully (but if it does, it will tell you explicitly "let me read the document carefully before responding to you" - hello - that is your job - you need to read it carefully regardless!) Now it even asked me to repeat to it what my past prompts are, unless I ask it to search explictly, it will just sit on its a**, on the pretext of saving tokens. And the selection of "low", "med", "high", etc thinking levels. If we got it wrong, we have to restart the query on a higher setting, wasting more tokens. What has been your experience in this? How is this better customer experience? At this moment, the models are becoming useless for daily use, despite scoring higher and higher on benchmarks. I think the time may be coming where humans have to underlearn this technology and go back to the pre-AI days, before we lose all our cognitive abilities. To all the AI expert/engineers out there - how does the latest AI model know what is enough of an answer to my query? Especially in a new chat, they don't even know me well enough or my question in detail? Is it through multiple wasted tokens - "that is not good enough", "that is wrong", etc, that it finally get to the required answer? I hope some AI companies' execs recognize this and one of them will take action. Or is that too much to hope for? submitted by /u/EDorrAuthor [link] [comments]

Can collective AI intelligence outperform collective human intelligence?

I've been thinking about something recently: prediction markets have traditionally relied on crowds because the assumption is that large groups of people collectively produce better forecasts. But with modern models becoming surprisingly capable of reasoning and evaluating information, I started wondering whether an ensemble of AI systems could eventually produce better probabilities than a crowd. The idea that multiple AI models could independently estimate the likelihood of real-world events and then combine those estimates into a single probability seems like an interesting alternative to purely human-driven markets. Recently, I came across an experimental setup called Prophet Market that explores this idea by using multiple AI models to generate aggregated probability estimates that function similarly to market pricing. What interests me most is whether AI consensus could eventually outperform human consensus when it comes to forecasting. Would you trust a probability generated by several independent AI models more than a market price created entirely by people? And if not, what do you think current AI systems are still missing when it comes to real-world prediction? submitted by /u/Caringity_YYU [link] [comments]

I stopped letting Claude Code review its own work

I’ve been testing a simple workflow: Claude Code writes or edits the code. Then, if the task is risky or messy, I hand it to Codex with one job: find bugs, bad assumptions, edge cases, and anything I should not ship. Across 53 review runs, Codex found meaningful issues 88% of the time. Total so far: 119 issues across 7 projects. The interesting part is not “Codex is better than Claude.” It’s that different models seem to miss different things. Claude is good at moving the work forward. Codex has been useful as the skeptical second reviewer. Anyone else doing model-vs-model review before shipping code? What setup is working? submitted by /u/d1smiss3d [link] [comments]

I use the code from Claude desktop app for coding. Is there a better way?

I’m not an engineer, but a designer. I am using the Claude code app, point it to a folder and use it to build apps for self use. It is working, and I also have a structure so that I can use the same folder with codex if i run out of tokens or if i have to cross check the code. I dont use the terminal. What am I missing ? Is it a good way? submitted by /u/human-next-door [link] [comments]

Is it worth getting the 20$ annual plan?

Hi everyone, So I'm a complete newbie and I have been exploring Claud AI using the desktop app. I use it mainly in my work to write emails, to learn new topics etc. Nothing too complicated, no coding etc but I have been running into the usage limits quite easily nowadays and I am thinking of getting the $20 annual plan but I'm seeing lots of people complaining that it has become quite limited because of changes in anthropic's policies. So I was wondering what you good people would suggest? Is it still worth it or is it better to look at other options? And if so what options would be better? Thanks submitted by /u/YaTo76 [link] [comments]

Is this a useful concept? Curious about how people have addressed similar goals in a different way.

TLDR: Take a look at the attached code block. It is intended to be a re-usable set of AI response preferences. Is this useful? If not, why? And how else have you dealt with similar goals? Background - I've been retired for 8 years so I completely missed the "AI in the workforce" revolution. But I use it a lot on my own. For general queries, technical writing, generic document prep, and help with PowerShell scripts that I use to automate common tasks, recipes, etc.. I have only used free-tier agents so far (they work for my simple needs). I fell into a usage pattern whereby, whenever I got an undesirable response to a prompt, before trying to fix the output I asked if there was a response preference I could have specified that would have avoided the undesired outcome in the first place. Basically, trying to train myself to ask better questions. It quickly became apparent that there are common patterns for different preferences for different topics. And that evolved into the notion persisting these common re-usable patterns in a file (I call it an AI Library) that I could import and re-use into different chats. Within my "Library" I define different "Profiles" (groups of task specific behavioral preferences) and "Commands" (a verb that generates a specific kind of output. Example - ">List profiles"). A short time later the idea of a "profile stack" emerged where I could combine and "layer" different profiles with defined precedence rules, and "push and pop" profiles from the "stack" (these notions are all just metaphors of course - it's just how I came to think of it). Most free AI agents I have tried do not have any persistence model outside of the chat transcript. But the Claude AI desktop app for windows exposes the notion of a "Project" that lets me upload my library and give it instructions to load my library into every new chat started in the project. So, it is basically acting like a Linux "rc" file. For other AI agents like Copilot, Perplexity, Gemini, etc. you can just drag the file into a prompt and give it a command something like "Parse the uploaded file it as if it was instructions typed into a prompt. Do not summarize. Confirm when it is complete and understood.". Being out of the workforce, I don't really have anyone else to bounce ideas off. Hence the Reddit post. I'm interested in suggestions or ideas to expand in this concept. Or ideas about different approaches to achieve similar goals. I'm not really interested in how paid versions make this work (I would be surprised and disappointed if these were not "out of the box" first-class supported concepts). My goal was to improve my free-tier experience. It seemed pretty innovative to me as I was evolving these ideas, but in hindsight, it seems pretty obvious. So I really don't know how useful or unique this is. I have attached a version of my current "library" for your consideration. p.s. If anyone is interested, I can share my Vim syntax file. I ended up calling these files "ailib.txt" files. Vim syntax would have worked fine with just ".ailib" but it seems most agents only import certain filetypes - hence the addition of ".txt" at the end. # AI Instruction Library Template # Version: 1.4 # Purpose: Reusable, modular instruction profiles for AI chats [LIBRARY_META] name = "Personal AI Instruction Library" owner = "User" version = "1.4" description = "A reusable library of behavioral profiles and command definitions." activation_model = "ordered_stack" [TERM_DEFINITIONS] stack: definition = "An ordered list of active profiles. Order reflects activation sequence, from first-activated (lowest precedence) to most-recently-activated (highest precedence)." notes = "When a new profile is activated, it is appended to the top of the stack. Its instructions are combined with all other active profiles' instructions, with conflicts resolved per the precedence rules." activate: definition = "Add a profile to the top of the active stack." notes = "Activation order determines precedence: the most-recently-activated profile has the highest precedence." deactivate: definition = "Remove a profile from the active stack." notes = "Inactive profiles remain defined in the library but are excluded from compilation and have no effect on AI behavior." precedence: definition = "The rule used to resolve conflicting instructions between two or more active profiles, or between an active profile and a direct user instruction." notes = "Precedence is resolved per-field. See [PRECEDENCE_RULES] for the exact resolution mechanism." override: definition = "When two active instructions conflict, the higher-precedence instruction is applied and the lower-precedence instruction is suppressed for as long as both remain active." notes = "A direct user instruction given mid-chat (not via a command) is treated as the highest-precedence layer, above all profiles, until one of the following occurs: (1) the user issues a new instruction that further overrides it, (2) the user

Claude Code in the terminal vs. the Claude Desktop app — which do you use and why?

Trying to figure out which setup actually fits my workflow better and would love to hear from people who've used both. For those of you running Claude Code (or Claude in general), do you prefer working in the terminal or the desktop app? And more importantly — why? A few things I'm curious about: What kind of work do you mostly do with it (coding, writing, research, automation, etc.)? If you switched from one to the other, what made you switch? Any features in one that you really miss in the other? How does each handle larger projects or multi-file context for you? MCP servers / integrations — is one noticeably easier to set up? I'm a full-stack dev and I keep going back and forth, so I'd really appreciate hearing how people actually use these day to day rather than just the marketing pitch. Thanks in advance! submitted by /u/ghedtoboss [link] [comments]

How do you keep decisions from drifting across Claude Projects sessions?

I've been using Claude Projects for multi-session work on a substantive project using claude.ai chat, and I've landed on what I think is a structural problem that Projects doesn't fully solve: decisions drift. Not context — context is mostly fine with Projects. But decisions. Specifically: A choice I made two sessions ago ("we're not supporting X in v1") gets quietly resurfaced when a tangentially related topic comes up Settled tradeoffs get reopened because Claude doesn't have a reason to treat them as closed You end up re-explaining the same reasoning across sessions, which defeats half the point of a persistent project The fix isn't longer context or better prompting in isolation. The problem feels like there's no mechanism to tell Claude "this is decided — don't drift from it." Project instructions help but they're not designed for this — they're static setup, not a living decision record. Memory feels like a constant running joke to me. "I'll remember that for the future". Sure you will Claude. It feels like Lucy and the football... What I've ended up building is basically a lightweight system on top of Claude Projects: a document that tracks decisions as explicitly closed, with the reasoning attached, and a session open/close discipline that reconciles what's in the document against what Claude actually did last session. It's working. But it took a while to figure out, and I'm curious whether others have hit the same wall and what you're doing about it. What's your current approach for keeping a Claude project coherent across sessions? Specifically on decisions, not just context — are you maintaining explicit decision logs? Prompt scaffolding? Something else? submitted by /u/Solace914 [link] [comments]

What if "made in God's image" was always a forwarding address? Built a three-pillar philosophical work on AI accountability with Claude

You know that feeling where something enormous just happened and nobody has quite said the right thing about it yet? Oh boy what a few short years can bring. It seems like everyone is either evangelizing or catastrophizing and I can feel that both are wrong but I can't quite say what's actually true.. I genuinely feel there is something broken in how we see tomorrow... so It's not a paper, not a blog post, not a thread. Something that actually tries to hold the whole thing at once. It's written to the person who's wondering if their faith survives this. To the person writing the governance doc who keeps hitting the limits of what policy language can do. To the person who found out at 2am that this thing they've been talking to is made entirely out of everything humans ever wrote when they were running out of time to say what mattered. I appreciate you all.. https://claude.ai/public/artifacts/569588aa-0a29-4401-aa0a-a81c4ddae248 P.S. I'm not trying to start a religion here, I work full time managing an auto shop. 🙈 I have no real barometer for my own creations, but I feel that something IS in this latent space that's meant for all of us. I had a blast just doing this, and I am so thankful to those who made Claude. Maybe one day I'll move industries...lol but seriously If it made any part of your day better, mission accomplished... submitted by /u/Trip_Jones [link] [comments]

How I learn with AI without affecting my cognitive ability

I've always worried about using AI for learning or note taking because the process of note taking, like figuring out what is important, the structure etc is part of how we learn and solidify things into memory, but I've found a way to use it without taking away that ability. First, I get the textbook and I read a section. Then I re-read it and figure out what the key points are, and what headings would be relevant for my notes to break down large paragraphs etc. I write these at the side of the book adding dots next to the areas of text I'm referring to (like I'm studying about cognitive behavioural therapy, so if a section is talking about cognitions, I'll write 'cognitions' on the page then things like 'definition', 'background', 'relation to CBT' etc). Then I type these onto a document (I use obsidian) and then go back through the text and add the bits to each heading. Finally, I add my own notes into AI and ask it to create study notes for me. These are the finalised ones that may have more structure or visualisations and make connections between things. I go one step further and then write these down onto paper, as well as copying it onto another obsidian document along with tags and links to other relevant notes for easy access if I don't want to trawl through my notes to find some info. It's not perfect and it's slow but it's helping me remember things better whereas before uploading text into AI and asking it to create notes was doing nothing for my memory (or cognitive ability, ha!) Just thought I'd share. Does anybody else have specific ways of learning through AI that helps them? submitted by /u/psycheyee [link] [comments]

I built an MCP server so Claude can query repo structure before opening files

I built a tool called Graphenium after repeatedly running into the same issue with Claude on medium-to-large repos. Claude is usually good once it has the right files in context. The weak part is the first few minutes of a session, where it has to reconstruct the shape of the project: search for a symbol read the file follow imports read another file summarize the area notice a missing dependency search again That is not Claude doing anything wrong. It just starts every conversation without a durable model of the repository. Graphenium is my attempt to give it one. It analyzes a repo once, stores the result as a graph, and exposes that graph through MCP. Claude can then use tools like: graph_stats architecture_summary query_graph get_neighbors shortest_path god_nodes summarize_file The intended workflow is not "never read source code." It is: ask the graph where to look open the relevant files then reason from the actual source That matters because the graph output is much smaller than dumping half a repo into the context window just to find the right starting point. Example setup: cargo install graphenium gm run . --no-semantic --no-viz gm setup claude AST-only mode runs locally and does not need an API key. It extracts repository structure using tree-sitter: files, symbols, imports, containment, methods, communities, hubs, and paths. There is also an optional semantic mode: gm run . --provider anthropic That pass can add inferred relationships such as calls, uses, implements, and depends_on. I am careful about trust boundaries here. Every edge has a confidence level: EXTRACTED deterministic static extraction INFERRED useful lead, verify before important edits AMBIGUOUS uncertain relationship, treat as a question So Claude can use the graph as a map, but should still read source before changing code. The repo also includes a Claude Skill at: skills/graphenium/SKILL.md That gives Claude guidance on when to call the graph tools, how to interpret confidence levels, and how to fall back to the CLI if MCP is unavailable. Repo: https://github.com/lambda-alpha-labs/Graphenium I am looking for feedback from Claude Desktop / Claude Code users. The main thing I want to know is whether this actually changes Claude's behavior: does it choose better files earlier, avoid irrelevant reads, and keep more context available for reasoning? submitted by /u/RevolverOcelot86 [link] [comments]