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Confused about Claude Cowork

Hi all! Just a brief introduction of myself, I'm someone who just discovered the world of vibecoding as a non-coder and it blew my mind. Vibecoding aside, AI and automating my life has been something that I've been trying to get into for the longest time and it's so daunting for me because literally I'm a tech noob. Like I know how to navigate a Mac, but anything else other than the absolute basic functionalities and troubleshooting, I'm not great. I've been watching lots of videos, and trying to absorb as much as I can, and I love the idea of Claude Cowork. However, the biggest thing I don't get still is that within Claude Cowork, there's Projects as well. From what I understand, the normal "Claude Cowork chat" is mainly used for one-off tasks, such as clean up my desktop or read these 5 PDF files and summarise them for me. Projects, however, is for ongoing work that you repeatedly go back to because it retains memory. Here's my question. As you can see, even for the normal Claude Cowork chat, I can still select the project file that I wanna work on. Like I don't really get why don't people just always go into Projects in that case because of the memory retention. Do I make sense? I don't really think I know what I don't know for me to phrase the question properly. https://preview.redd.it/4jakruze1b3h1.png?width=680&format=png&auto=webp&s=b1960483acaa8e2c8295067ed5c25c358660b3bd Separately, I see all these videos about creating these very detailed Claude.md, Memory.md files. Are those super necessary? I'm just a simple guy and honestly I don't even know what do I wanna automate or which part of my life am I automating. I have no need to sort out calendars, I have no need to sort out emails. All of the important events are usually work and I can't link Claude to my work email. My personal events I can all remember off the top of my head. But I'm trying to figure it out as I go. I think I definitely can have some good use off this. Another question I have is - for all the Projects that I create, I can give them instructions. For example, how does that really differ from the main set of instructions I gave Claude Cowork via settings and if it does differ, how can I get the project to reference the "core framework" that I want Claude Cowork to always work within regardless of the topic for each projects? Also: How does Claude Cowork interact with Claude Code? Am I able to build dashboards or even vibecode simple apps via just Claude Cowork's projects? Sorry I know this is a lot, just a really curious learner trying to get the hang of things! submitted by /u/Ok-Vermicelli-1351 [link] [comments]

38. real estate team of 6 in omaha. claude is the reason my team forecast got accurate for the first time in 3 years.

omaha NE. 11 years residential real estate. running my own team within a brokerage for 2 years. 6 agents including me. combined volume last year ~$42M. ~$1.1M team GCI. for the first 2 years running this team, my quarterly forecasts were wildly inaccurate. q1 i would forecast $280k team GCI and we would close at $190k. q2 i would forecast $310k and we would close at $410k. variance was always 30-40% one direction or the other. i could not figure out why. i was using market data, our pipeline, recent comps, and intuition. nothing was working. in september i started using claude to help with the forecast. what i did differently. step 1: built an ai quarterly forecast deck (Gamma) with claude. structured around 6 inputs i had not been tracking together: current active listings, current pending sales by stage, my agents' weighted pipeline, recent local comp activity, mortgage rate environment, seasonal historical patterns. step 2: claude pulled patterns from my own 2 years of bad forecasts. asked me what had been different in the months where i over-forecast vs under-forecast. surfaced that i had been consistently overweighting "hot" pipeline conversations from my agents and consistently underweighting the seasonal patterns. step 3: claude built a forecast model that weighted the 6 inputs based on what had actually predicted closings in my historical data. the weights surprised me. agent-reported pipeline confidence was much less predictive than days-on-market in the local comps. i had been listening to my agents more than to the market. what changed. q4 forecast: $320k. actual: $311k. ~3% variance. this was the most accurate forecast i had ever shipped. not because my judgment got better. because i stopped weighting the wrong inputs. q1 2026 forecast (in progress): $340k. we are 6 weeks in tracking close to that. what i learned about non-tech founder use of claude. most non-tech founders i know use claude for writing (drafting emails, drafting content). that is fine but it is using ~10% of what claude can do. claude is best at finding patterns in your own decisions and data. specifically the decisions you have been making poorly. it does not have ego. it will tell you that you have been overweighting an input that does not predict outcomes. a human consultant might soften that feedback. claude does not. i was scared to ask claude "what have i been getting wrong" for ~6 months because i did not want the answer. when i finally asked, it told me. fixing the answer has been worth ~$100k of revenue accuracy this quarter alone. for other non-tech founders. ask claude what you have been getting wrong about your business. paste in your historical decisions and outcomes. let it find the pattern. then fix the pattern. uncomfortable. extremely valuable. submitted by /u/Temporary-Prior7384 [link] [comments]

My Mac now has a wake word for Claude Code

Honestly this started as a weekend hack because I was tired of typing the same kind of prompts into Claude Code over and over. I wanted to just talk to it while making coffee. So I rigged up a wake word (Yabby), a WebRTC voice loop for the conversation, and an actual plan-approval modal that pops up before any agent runs so I can vet what's about to happen first. That was the plan. Two weekends later it had quietly turned into something weirder. The voice loop now talks to a "lead agent" that breaks the work down into a discovery phase, a plan, then it recruits a small team a manager or two, and sub-agents that actually do the work. They run in parallel where they can, sequentially where they can't, and when a sub-agent finishes there's an auto-triggered review pass (5 second debounce so they don't pile up). The lead agent watches the whole cascade and reports back by voice when everything's QA'd and done. Each agent runs its own Claude Code session under the hood with its own thread, so the conversations don't bleed. Watching three agents work in parallel on the same project last night was genuinely uncanny. One of them caught a bug another one had written. That part I really didn't expect. Things I still hate about it: - Speaker verification is fiddly. Cosine-similarity threshold on the speaker embedding is annoying to tune too tight and it rejects me when I have a cold, too loose and it'll wake for anyone in the room. - French was the default locale because I wrote it that way. Slowly fixing it. - Background tasks dying when the parent Claude Code CLI exits was a nightmare to track. Ended up writing an OS-level PID watcher with a bookkeeper shell script just to know which long-lived servers had crashed. - Lead agent occasionally over-plans tiny tasks. Ask it to rename a file and you get a four-phase project plan. Working on it. Stuff I'm still figuring out: how to make the QA phase less chatty, whether to let sub-agents recruit their own sub-agents, and how to keep the voice latency under 300ms when the Realtime API gets cranky. Curious if anyone else has tried voice-controlling Claude Code? Anthropic rolled out their own voice mode to 5% of users a couple weeks back and I keep wondering how they'll handle the multi-agent piece does anyone here have access to that rollout yet? submitted by /u/Interesting-Sock3940 [link] [comments]

How do you guys avoid Claude always thinking newer LLMs don't exist?

Hey all, so I've been experimenting a bunch with different LLMs, specifically for creative tasks, i.e. RP and so forth, by letting Claude Code run experiments autonomously, to figure out best prompts, and such. This has been fun, in particular with DeepSeek V4 Pro, which is a true bang for a buck. However, despite reminding Claude that v4 Pro exists, mentioning it in CLAUDE.MD and so forth, every single time, it still falls back to older DeepSeek versions because those are known by it. So often I catch it mid talking saying "let's make a call to DeepSeek-r3 (or whatever the older one was called)" and stop it, reminding it to look at newer versions. Same for Open AI LLMs, it's basically stuck at GPT-4o. I fully understand knowledge cutoffs and all that, but it's a bit annoying because even when I tell it to research LLMs, at least half the list is depreciated or old LLMs. Any way to cope or handle this? It's super annoying because sometimes, despite me asking it to research latest and such, I just catch it late, and then suddenly my entire research is undone lmao. submitted by /u/Toedeli [link] [comments]

I ran Claude Desktop for a month and 73% of my Anthropic bill was MCP tool calls, not chat

Set up Claude Desktop with Playwright, filesystem, GitHub, and a few other MCP servers about 6 weeks ago. Just hit my first $200+ month and went to figure out where it went. Surprise: chat completions were only $54. The other $146 was tool calls — Playwright alone was $89 because the agent kept opening pages with massive DOMs and the whole thing got piped back into context. Top 5 by cost: playwright/browser_navigate — $43 playwright/browser_snapshot — $46 filesystem/read_file — $22 github/get_pr_diff — $18 brave-search — $11 Lesson learned: cap your Playwright context. Disable browser tools when not actively browsing. The model bills you for what comes back, and DOMs are huge. How are others budgeting this? I genuinely had no idea this was the breakdown until I started measuring. submitted by /u/Slow-Relationship897 [link] [comments]

Has anyone else noticed certain words make AI agents actually listen?

Been working with AI agents for about 2 years and I keep noticing word choice matters way more than I expected. Simple example that got me thinking. "Don't do Y until X is done" works maybe ~75% of the time for me. But "Y has a dependency on X" and compliance jumps way up (well into the 90s). Same instruction, totally different result. I noticed this is a very real thing on a project where I'm helping improve productivity agents (think emails, slack, Instagram, sheets, docs), so it's not really coding tasks. My guess is certain words pull from different training contexts. "Dependency" comes loaded with software and project management patterns where order actually matters. "Don't" gets ignored because humans ignore it constantly in real life and the model learned from that. But honestly I'm still figuring this out and would like to know more about it if anyone has any thoughts. It might be basic prompt engineering to some, but I'm curious about whats happening under the hood or if anyone else has any similar words that seem to improve accuracy/attentiveness. submitted by /u/Aggravating-Dog5022 [link] [comments]

Claude issues with design and MCP

Hi everyone, I am trying to launch a digital design magazine on my domain koncepto.dk. My goal is to achieve an ultra-clean, fjerlet, minimalist aesthetic design, meaning a tight, asymmetrical grid, lots of white space, subtle 1px gray borders dividing the sections, and clean typography. Where we are right now: I have actually built the entire frontend design myself. I have a set of fully functional, pixel-perfect, static HTML/Tailwind CSS files (including index.html and article-template.html) that look exactly like the high-end design magazine I want. The Problem (Claude + MCP issues): I am using Claude with an active MCP (Model Context Protocol) connection to my server, where I have a fresh WordPress installation with the Blocksy theme. The goal was to have Claude use its MCP tools to implement my static HTML/Tailwind design directly onto the live site. However, Claude is completely dropping the ball. Instead of injecting my raw HTML structures or correctly translating my Tailwind grids into a clean WordPress template, the AI keeps reverting to "lazy mode." It just activates Blocksy’s heavy, bulky, out-of-the-box standard blog layouts, tweaks a few colors, and claims the job is done. The result looks like a generic, cluttered 2010 WordPress blog nowhere near the elegant Yanko Design vibe in my source files. On top of that, the WordPress Customizer ("Tilpas") is completely crashing due to server/database overhead from the MCP requests, so we have to do this directly via code/file injection. What we are trying to figure out: How do we successfully force Claude via MCP to stop using the theme's built-in layout engine and instead use my raw HTML/Tailwind files as the actual template? Should we completely ditch Blocksy/WordPress and just upload the raw HTML files directly to public_html as a static site? Or is there a proven prompt/workflow to make Claude map standard WordPress post data (the_content(), the_post_thumbnail(), etc.) directly into a custom-built, blank PHP template containing my exact HTML/Tailwind layout? Any advice from people using Claude/MCP for WordPress development would be highly appreciated. I have the perfect design ready in my hands, but the AI integration is currently acting as a bottleneck rather than a tool. Im SO stuck. Its like Claude tells me all is ok, but nothing changes online Thanks in advance! submitted by /u/Adventurous_Run_6310 [link] [comments]

Why We Build

One silver-lining to the dead internet we're living in, today, is that it's very quickly teaching us that we can't rely on our senses as much as we believe we can. It's not healthy to always live in skepticism, but it is necessary in a World where you don't know what's up or down anymore. That's why we need great minds to focus their attention on solving the problems associated with credible information sharing without it becoming some centralized playground designed to look like the free-flowing exchange of ideas. If we don't solve for that, then I guess we're heading into a future that a small handful of people want because elections or public opinion will no longer matter. One of the biggest focuses in AI should be in figuring out how to get it to provide deep credible knowledge in specific domains that can be best applied to the problems we're trying to solve. Sure, it can do this with enough fenagling, but what I really mean is having something easy for everyone to use like Perplexity or Gemini, only it doesn't simply find consensus information from the internet using all these black box methods that are owned by major corporations. Instead, it should use direct knowledge from domain experts who structure and cite their material and as users, we should be able to backtrack all of it, including the original author. And all of this should be achievable by simply engaging with a chatbot agent that can reliably go out and help me discover all of these things. Also, we shouldn't have to simply trust that the application works. We should be able to go in and see exactly how it's working. This way, the public can audit the systems we're relying on for grounding our worldviews. That, to me, is where we should be if we really want to break from the chains of propaganda and reclaim our genuine thoughts about how we ought to live. The alternative independent media space was co-opted long ago and now all of the feeds keep us in a state of perpetual dislocation from our friends, family, communities, new solutions, and better approximations to the truth. We exist in a walled-off digital pasture. But if regular people who are smart and capable enough decide to leverage this new technology, then we can break through the fencing and finally live in a world where discovery-based researching and learning can be easier than Google, which could eventually individuate society again, like how it was before, instead of keeping us clustered into specific groups based on our viewing preferences. That's why my brother and I got into this business. Yeah, sure, we also wanna make a buck so we can retire with dignity. That's true. But the drive has always stemmed from wanting to figure out a better way for people to share hidden insights and create things that are bigger than they thought they could handle. We have a long way to go, but we're making the first small steps, even if it isn't obvious, just yet. Bottom line, though? Humanity must figure out a way to help us master the means and methods of discovery-based knowledge acquisition, execution, and immediate distribution of information based on relevancy and needs from those who search instead of those who passively soak information in from the curated feeds. And all of this needs to be easy enough for a 12 year-old to do. If anyone else is working on this problem, we'd love to hear your thoughts, even if it's through a DM. We're living in the most exciting times, but with adventure, comes danger. So maybe, idk. Let's make it more fun and less hazardous, so that we can, at least, live long enough to re-tell this great story that we're all a part of. submitted by /u/CyborgWriter [link] [comments]

Claude working autonomously

Goodmorning, Has anyone figure out how to configure Claude so that it runs autonomously, almost like Openclaw? I wanted to figure out if it could just autonomously respond to LinkedIn messages and reach out on my behalf? I know i can do this within cowork with mcp servers and tools but didn’t know if managed agents or the SDK would be my best option to try and create this full system submitted by /u/Perfect-Cricket6506 [link] [comments]

I made a Claude Code plugin that draws matplotlib figures in that soft-pastel "alignment research blog" style

You know the look — the figures in Anthropic's research posts. Bold sans-serif titles, scatter points under a smoothed trend line with a shaded band, those bars with the slightly rounded tops, little ↓better badges in the corner. I kept wanting my own plots to look like that and kept rebuilding the same matplotlib boilerplate, so I packaged it into a Claude Code skill. It's called nice-figures. Once it's installed, you just describe the plot you want and Claude picks it up automatically: "training-curve plot of these RL scores with a smoothed trend and shaded band, research-blog style" "grouped bar chart comparing three models across four evals, with the rounded bar tops" Bring your own CSV/arrays and it maps them onto the closest chart; describe a figure with no data and it generates a clearly-marked synthetic placeholder. Under the hood it's one skill plus a small style helper (matplotlib + numpy, no other deps) and 16 chart recipes — training curves, grouped bars, ROC, heatmaps, scaling-law scatter, forest plots, Pareto fronts, etc. White background by default so the output is paper/conference-ready, with an opt-in cream background for the blog look. Install: /plugin marketplace add Mapika/nice-figures /plugin install nice-figures@nice-figures Repo (MIT, example images in the README): https://github.com/Mapika/nice-figures Built it for my own use, figured others might want it. Happy to take feedback or recipe requests. submitted by /u/Mapikaa [link] [comments]

The actual plan of the AI companies:

submitted by /u/EchoOfOppenheimer [link] [comments]

Storyboard generated from GPT image 2.0

I gave GPT a set of prompts that I found a bit too complicated, and to my surprise, it generated content that matched perfectly. I'm very curious about how GPT Image 2.0 works behind the scenes, and how it can understand and produce high-quality images so quickly. prompt：**PROJECT FILE: HIGH-ALTITUDE ASCENT // PREMIUM HARDSHELL CAMPAIGN** **FORMAT: ARRIRAW 4.5K / KODAK VISION3 50D 5203 EMULATION** **DIRECTOR'S PRE-PRODUCTION VISUAL BOARD** --- ### Top Left Area | Character Lock Zone **[SUBJECT]** 35-year-old male mountain guide/extreme climber. **[WARDROBE]** Top-of-the-line professional jacket (matte rock grey with minimal dark orange taped details), heavy-duty climbing harness. **[VIEWS]** - **Front:** The jacket is fully zipped up, hood pulled up, showcasing a three-dimensional cut and natural drape. - **Side:** Shows ample shoulder and arm movement without bulkiness. - **Back:** Shows the windproof and breathable back panel structure. - **3/4 View:** Dynamic standing pose, holding an ice axe. **[REALISM NOTES]** Realistic human bone structure, slightly asymmetrical. The face has the rough texture of high-altitude red and sun-dried skin, with clearly defined pores and stubble with a frosty look. Rejecting perfect plastic skin, rejecting CG aesthetics. Like a real makeup test photo. --- ### Top Right Area | Expression + Motion Keyframes (EXPRESSION & ACTION) **[EXPRESSIONS]** **Focused:** Slightly furrowed brows, resolute gaze, staring at the rock face above. **Bracing:** Squinting against the strong wind, facial muscles tense. **Breathing:** Lips slightly parted, exhaling real white mist. **[ACTIONS]** **Hood Adjustment:** Pulling the drawstring of the hood with one hand. **Ice Axe Swing:** Arm raised high with force, no pulling sensation under the armpits of the jacket. **Brushing Snow:** Brushing snow off the shoulders, demonstrating the fabric's water-repellent properties. --- ### Upper Middle Area | CAMERA PLAN **[GEAR]** ARRI Alexa Mini LF + Master Prime lens set. **[LENSES]** 24mm (wide-angle environment), 50mm (medium-range tracking shot), 100mm Macro (fabric close-up). **[MOVEMENT PLAN]** - **Shot A (Drone/Crane):** A wide, overhead view, slowly pushing in along a snow-covered ridge. - **Shot B (Handheld):** Shoulder-mounted camera, following the character's movements, with realistic breathing and slight shaking. - **Shot C (Slider):** A close-up panning shot close to the clothing, showing water droplets sliding off. --- ### Central Main Area | Continuous Story Shots (STORYBOARD: 8 PANELS) **[PANEL 01]** - **Shot:** 01 | 24mm | Wide Shot (EWS) | Slow Push-In - **Action:** A tiny figure struggles through a massive natural storm on a snow-covered ridge. - **Detail:** Strong atmospheric perspective; the wind and snow create a realistic fog effect; slight chromatic aberration at the edges of the image. **[PANEL 02]** - **Shot:** 02 | 50mm | Mid Shot | Shoulder-mounted tracking shot - **Action:** A man walks against a blizzard; the strong wind whips against his rain jacket, creating realistic physical wrinkles on the surface, but the overall silhouette remains sturdy. - **Detail:** Noticeable film grain; the snow-capped mountains in the background are slightly out of focus. **[PANEL 03]** - **Shot:** 03 | 100mm Macro | Extreme Close-up (ECU) | Fixed Macro - **Action:** Icy snowmelt hits the shoulders of the rain jacket. - **Detail:** The lotus effect is realistically rendered—water droplets condense and quickly roll off the matte micro-ripstop fabric without penetrating. **[PANEL 04]** - **Shot:** 04 | 85mm | Close-up of face (CU) | Slow motion - **Action:** The man stops and looks up. Real ice crystals cling to his eyelashes, and his breath dissipates at his collar. - **Detail:** Natural skin tone, without excessive blurring; realistic catchlight in his eyes reflects the snow wall ahead. **[PANEL 05]** - **Shot:** 05 | 35mm | Low Angle Full | Handheld, low-angle shot - **Action:** He swings his ice axe into the ice wall, climbing upwards. - **Detail:** Emphasis on showcasing the flexibility of the jacket during vigorous movement; no feeling of restriction; realistic light and shadow highlight the garment's three-dimensional cut. **[PANEL 06]** - **Shot:** 06 | 100mm Macro | Close-up Detail (Insert) | Shallow Depth of Field - **Action:** A heavily gloved hand pulls a waterproof zipper across the chest. - **Detail:** The matte waterproof rubberized finish of the zipper and the clearly visible scratches on the brushed metal zipper pull exude a strong sense of industrial design. **[PANEL 07]** - **Shot:** 07 | 50mm | Over-the-Shoulder Lens (OTS) | Slow Zoom In - **Action:** Over the man's shoulder, we see him finally reaching the summit, sunlight piercing through the clouds and shining on him. - **Detail:** Realistic lens flare, not exaggerated, natural glow. **[PANEL 08]** - **Shot:** 08 | 35mm | Mid Shot | Still Camera - **Action:*

Superpowers and Reviewing

I find that after the superpowers plugin (brainstorming) makes a spec, it typically does a short self-review and says let's move to the plan. If I ask it to review against our convo and claude.md again, it will find things - sometimes big things - and if I do it 2-4 more times it consistently finds things that are important to me. The same is true after the implementation plan. And of course the same is then true with the code! I know this is par for the course, and I'm burning tokens when I use it (which is only for the more complex things), but can I make it do this itself before it even gets to me next time on each of these 3 steps? I want it to literally ask it to review again over and over again until it only finds low-impact issues, essentially. I figure people will say put it in claude.md - but this thing never follows instructions from claude.md anyway, I have to always tell it to first look at it and review against it before it realizes it didn't do what claude.md says. Looking for tips here, thanks! submitted by /u/nothingnowherenever7 [link] [comments]

Claude Code has been writing every session to disk since day one. We indexed it.

Go look at ~/.claude/projects/. There's a JSONL file for every session you've ever had. Every turn, every tool call, every file touched, every response. All of it, append-only, going back to your first session. Ours goes back to January — 57MB, 1,026 sessions, 76,000 turns. Just sitting there the whole time. We didn't get tipped off. We just looked. The format is clean too. Each line is a JSON object — role, timestamp, content, tool calls, everything structured. It's not logs in the "good luck parsing this" sense. It's a complete episodic record. If you had a three hour session last Tuesday where you figured out something important, that conversation exists in full fidelity on your drive right now. You just have no way to get back to it. So we built an indexer. SQLite+FTS5, temporal edges between turns, MCP server on top. From inside any Claude Code session now: search_sessions("remember when we fixed that auth bug last month") recall_session("a8f2c441") thread_recall(root_id, depth=8) That last one does a BFS traversal through the temporal edge graph to reconstruct a thread across session boundaries. The "I told you this two weeks ago" problem just disappears. The data was never gone — nobody had built the recall layer on top of it yet. We also support importing conversations.json from the claude.ai data export, so your web chat history lives in the same index as your CLI sessions. The other half is compaction. Everyone who uses Claude Code seriously has felt this — context fills up, compaction fires, and you're suddenly explaining your whole project again to something that should already know. We wired the full hook chain to stop that from happening. The thing nobody writes down is that transcript_path in the PreCompact payload isn't always populated at hook fire time. You build your whole save logic around it, ship it, and then hit silent failures you can't explain. We did exactly that. The fix is that Stop needs to write a checkpoint on every single turn, not just at session end. Then when PreCompact fires it always has something fresh to fall back to no matter what. Then SessionStart reads the source field — "compact" means compaction just fired, "resume" means the app restarted, "startup" is a fresh session, "clear" is intentional. Each gets different behavior. None of this is documented anywhere, you just have to figure it out. The net result: compaction stops being a hard reset. It's a cache miss. We've also been in the middle of the upstream conversation at anthropics/claude-code#47023 — seven independent memory projects, all built by different people, all independently hitting the exact same walls and arriving at the exact same hook requirements. Bella, NEXO Brain, Cozempic, world-model-mcp. None of us were coordinating. We all just needed the same things. The formal hook spec is getting worked out there if you want to follow it. Repo: https://github.com/Haustorium12/continuity-v2 — MIT, hooks take about five minutes, MCP server is one Python file. Happy to answer questions. submitted by /u/haustorium12 [link] [comments]

Who am I even supposed to trust when it comes to the future of AI?

I am a PhD student (not in AI) and am usually alright when it comes to studying a topic I don't know much about. But it seems that because AI is so highly discussed nowadays, it's impossible to get a good gauge of what the rational scholarly consensus is regarding its and our future. I am constantly bombarded with people saying that at best most jobs are replaced and the future is a dystopia, and at worst AGI/ASI is achieved and we all are killed by a bioweapon or something. It honestly has me terrified, especially when I see a lot of figures in the AI sphere, including academics, seem to think that there are reasonably high "p(doom)"'s (what a horrifying concept that is). How am I supposed to parse all of this? Are there any actually level-headed people? Or are the people shouting about doom actually the level-headed ones? Compared to climate change, at least there are the IPCC reports which have laid out best guesses on what will happen. They're not perfect, but at least they exist. submitted by /u/QuantumLand [link] [comments]