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OpenAI Pro Plan Pencil Gift Confirmed

Just got my email confirmation for the OpenAI pencil gift, and it includes a tracking number. Pretty excited to see what actually shows up. Has anyone else received their confirmation or shipping email yet? I’ll update once it arrives submitted by /u/SodaAnSumWii [link] [comments]

Anthropic Announced vs current compute capacity (Sources Below)

source list: Google Cloud TPU deal — up to 1M TPUs, “well over 1 GW” expected online in 2026 https://www.anthropic.com/news/expanding-our-use-of-google-cloud-tpus-and-services https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services (Anthropic) Fluidstack / Anthropic $50B U.S. AI infrastructure — Texas + New York, sites coming online through 2026 https://www.anthropic.com/news/anthropic-invests-50-billion-in-american-ai-infrastructure https://www.fluidstack.io/about-us/blog/fluidstack-selected-by-anthropic-to-deliver-custom-data-centers-in-the-us (Anthropic) Microsoft + NVIDIA deal — $30B Azure compute commitment + up to 1 GW additional capacity https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia-and-anthropic-announce-strategic-partnerships/ https://blogs.nvidia.com/blog/microsoft-nvidia-anthropic-announce-partnership/ (The Official Microsoft Blog) Google + Broadcom next-gen TPU deal — multiple GW starting 2027; Broadcom SEC filing says ~3.5 GW https://www.anthropic.com/news/google-broadcom-partnership-compute https://investors.broadcom.com/static-files/c906d370-921b-4bc2-bb7b-57877dfcf1ae (Anthropic) Amazon / AWS deal — up to 5 GW, nearly 1 GW by end-2026 https://www.anthropic.com/news/anthropic-amazon-compute (Anthropic) AWS Project Rainier — operational now, nearly half a million Trainium2 chips; Claude expected on 1M+ Trainium2 chips https://www.aboutamazon.com/news/aws/aws-project-rainier-ai-trainium-chips-compute-cluster (Amazon News) SpaceX / Colossus 1 — all Colossus 1 compute, >300 MW, 220k+ NVIDIA GPUs within the month https://www.anthropic.com/news/higher-limits-spacex https://x.ai/news/anthropic-compute-partnership (Anthropic) Independent reporting for SpaceX deal https://www.reuters.com/business/retail-consumer/anthropic-unveils-dreaming-feature-help-its-ai-agents-self-improve-2026-05-06/ (Reuters) submitted by /u/Business_Garden_7771 [link] [comments]

Former OpenAI Staffers Warn xAI's Poor Safety Record Could Complicate SpaceX’s IPO

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

Anyone else feel like Claude has gotten noticeably worse lately?

Anyone else feel like Claude has gotten noticeably worse lately? I’m not trying to start an AI war or anything — I genuinely used to prefer Claude for a lot of tasks (max x 20 plan). It felt more thoughtful, better at long-form reasoning, and better at keeping context across conversations. I’ve been using it heavily to work on strategies for promoting my app, Impulse Stop Habits — brainstorming growth ideas, positioning, onboarding flows, marketing angles, content funnels, etc. So I’ve spent a lot of hours talking to it over long sessions. But over the last few weeks, I feel like something changed. Now I constantly run into: - forgetting context after a few messages - contradicting itself - hallucinating details confidently - missing obvious instructions - giving generic “safe” responses instead of actually thinking - randomly ignoring parts of prompts - coding mistakes that weren’t happening before And I’m not talking about abstract “AI vibes.” I mean real workflow-breaking stuff. Example: Claude suggested using Reddit as a major acquisition channel for ma app (IMPULSE: Stop habits). The problem is that a lot of addiction / habit-recovery subreddits explicitly ban promotion. We actually tested posting in other allowed subreddits and measured the results — basically no meaningful conversions or traction. Despite already discussing that and reviewing the results together, Claude later continued recommending Reddit growth strategies again as if none of that prior context existed. Only after I reminded it: “we already tested this, and it didn’t work” did it suddenly apologize and completely change the strategy. That’s the part that feels different to me now: it often can reason correctly, but only after being manually reminded of a lot of context that was already established earlier in the conversation. Sometimes it honestly feels like the model is “tired” after a few exchanges (i am even texting: “You’ve tired, restart and use 100% of what you can”. And a couple of times it confirmed that worked on 10% only 🤣). Like the coherence just degrades mid-conversation. And this becomes especially obvious during deep strategy discussions, where context really matters. I’ll spend 30–40 minutes building up nuance around the app, target audience, monetization, creative strategy, and then suddenly it starts responding like it forgot half the conversation. The weirdest part is that older discussions about Claude were praising it specifically for context retention and nuanced reasoning — which is exactly where it now feels weaker to me. Am I imagining this, or are other people seeing the same thing? Curious whether this is: - heavier load / inference optimization, - aggressive safety tuning, - context compression, - model routing changes, - or just nostalgia + expectations increasing over time. Could send proofs in DM because they contain bad words 🤣 submitted by /u/Party_Nectarine2506 [link] [comments]

claude vs wingman after 1 month, they're not really the same category of tool

Been paying for both claude pro since the start and wingman for ~3-4 weeks and finally figured out why I don't feel guilty about it. Claude is where I do deep work ie long-form drafting, research, anything that needs me sitting at the laptop with coffee and 90 mins of focus. Wingman is the 50 small things I burn through during the day, like replying to a vendor email between meetings, "remind me to call X tomorrow," summarizing 4 unread group chats, drafting a quick linkedin reply on the train. They're not competing for me. One is a desk, one is a phone. People keep asking "which one do i cancel" and honestly neither, they do different jobs. The mistake is treating every AI tool like it has to replace every other one. Anyone else running both for similar reasons? submitted by /u/vedantk21 [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

I built a free AI chat app that keeps a "Context Bible" so your conversations don't drift - feedback welcome

Hi folks! Built something this week and want to put it in front of real users before going further. It's called Protext: an AI chat app that keeps a live "Context Bible" alongside your conversation. The Bible updates after every reply and gets injected as memory before every message, so long chats don't drift and lose the thread. No subscription. No backend. Bring your own Anthropic API key. (Only works with Claude at the moment) https://zaedre.github.io/Protext/ Would love to know: does it hold up in a real session? Where does it break? What's missing? submitted by /u/trollinginfidel [link] [comments]

Pope Leo x Anthropic: Pope Leo to issue text on human dignity and AI with Anthropic co-founder

submitted by /u/Objective_Farm_1886 [link] [comments]

I used Claude AI to build an $86 million underground bunker bible. I have autism. This is my happy doc.

It all started with the floor plan of a real, existing Cold War AT&T Long Lines underground hardened relay station. 54,000 sq ft across three underground levels, although I took editorial decision making to move it to a ridge in rural West Virginia, I kept its blast-rating, which was set to survive a 20 megaton airburst at 2.5 miles. That was the seed. Full scale prepper autism did the rest. It has since morphed into 3 spreadsheets — 86 tabs total: • A food inventory across 20 categories tracking every freeze-dried and #10-can product I can find — ancient grains, heirloom legumes, 7 pasta cuts, dehydrated everything, shelf-stable cheese, the works • A supply inventory with 3,466 line items across 36 categories — water systems, medical, dental, pharmacy, livestock, food production, barter metals, recreation, and yes, a full pest control and IPM tab • A 30-section infrastructure specification with every system in the building engineered out I fed it 150+ product manuals and parts order forms. The generator fleet alone is 13 units — 10× Cummins C150N6 propane-primary, a C500N6 500 kW surge unit, and 2× diesel emergency fallback — all Cummins for parts commonality. Battery bank is 4,500 kWh LFP across 10 named banks (A through J, each with a designated role). There’s a 400,000 gallon underground propane farm across 40 ASME tanks in 8 clusters — I learned the exact burial incline and setback distance required to keep groundwater clean if a tank lets go. 120,000 gallons of diesel backup. 88 kW of solar. A 1,000,000-gallon internal water reserve fed by a 300-ft artesian well. Propane endurance: ~30 years normal ops with solar. Sealed-mode runs 8 to 4.5 years depending on scenario. I actually set up a real LLC (online, $99) just to get access to US Foods and Sysco order forms so I could upload real commercial pricing and stock the food tabs more accurately. My original “what would I do if I won $10 million” thought experiment is now an $86,200,497 projected build cost. That number is real. It comes from 24 budget sections with make/model line items, freight, install, and commissioning costs for everything from the Kubota K-Series MBR wastewater trains to the American Safe Room blast doors (14 of them, 50+ psi NBC/EMP-rated, Kaba Mas X-10 cipher locks) to the surface greenhouse. Claude turns vague ideas into engineering-grade detail — cross-references, failure modes, zone-specific storage rules, propane endurance by operating scenario, spare parts matrices. It’s like having a tireless survival engineer who genuinely loves spreadsheets. I’ll say “scan all sheets row by row for any item that lacks a minimum stock level” and it just… does it. Thoroughly. Every time. No complaints. So much of this is typed stimming. I’ve had exhaustive conversations with my psychologist about it — she’s aware, but not alarmed, and honestly the resulting digital bunker bible is scarily comprehensive. It even has a cover tab now. Black and amber, Courier New, classified-document aesthetic. Because of course it does. What’s the most unhinged rabbit hole you’ve gone down with AI? submitted by /u/Unable_Internet4626 [link] [comments]

These 9 Building Blocks Turned Claude Code From a Chat Into a persistent OS

Most developers Claude gurus use Claude Code one project at a time. I run 18. Not 18 sessions. 18 instances of the same OS, each running a different business, all sharing one skeleton I update once and propagate everywhere. Most developers treat Claude Code as a smarter editor. That's where it all goes wrong and you get frustrated. Claude Code becomes a real operating system the moment you stop thinking of sessions as the unit of work and start thinking of the whole environment as a substrate you build on top of. Here are 9 building blocks I use. The thesis is at the bottom. Build a skeleton with selective propagation, not a project. Most developers build one project per Claude Code workspace. I built a template instead. It has plugins, rules, agents, hooks, schemas, commands. When I start a new business I clone it and the new instance inherits the entire OS. Right now I run instances for: strategy, product, marketing website, threat intelligence, three consulting clients, a personal brand layer. Each one boots with the same DNA. Each one diverges on canonical files, memory, output, and project state. None of them bleed into the others. The sync mechanism is the load-bearing part. The update CLI pushes plugins, rules, agents, hooks, schemas. It never touches memory, output, canonical, or my-project. Those are the parts of an instance that accumulate. Without selective sync you have two options: rebuild every instance on every change, or never update. Both are dead ends. If you build features into one project, you wrote a project.If you build features into a template that propagates, you wrote an OS. I'm one person operating eighteen versions of myself. Move state out of prompts and into code. LLMs are bad at remembering. Code is designed for it. Most AI workflows leak state into the prompt. Voice rules. Style preferences. Banned words. Recent decisions. Eventually you hit context limits or contradictions. I moved as much state as possible into MCP servers. Voice linter. Lead scorer. Schedule validator. Loop tracker. They run in Python, return structured data, not hallucinations. Rule of thumb: if you've explained it to Claude more than twice, it should be code. Use receipts, not status fields. This one took me the longest to figure out. Every workflow I had was claim something is done. Issue marked closed. PRD marked shipped. Test marked passing. The problem: the LLM can claim anything. I rebuilt the system around receipts. An issue can't reach verified until a script runs and writes a verification record. A PRD can't archive until every accepted finding has a receipt. A morning routine can't close without log entries from every phase. Receipts get written by code, not by the model. The model can't lie about whether code ran. Build a wiring-check gate. Half-built features rot. In a normal repo you notice because something breaks. In an AI repo nothing breaks. The half-built feature sits there and Claude pretends it works. I built a /wiring-check command. Before any task counts as done, it checks: every new skill has a trigger, every new hook lives in settings.json, every new MCP tool sits in the server, every new bus file has a producer and a consumer. "I think it works" fails the gate. "I ran X, got Y" passes. Make rules auto-load, not slash commands. If you have to type /voice to apply voice rules, voice rules will not get applied. Rules in .claude/rules/ load automatically. The voice rule fires on outbound text. The AUDHD rule fires on anything I'll act on. The social-reaction rule fires when I share someone else's post. No remembering. No willpower. Lint style in code, not in prose. I wrote a voice document once. Claude ignored half of it. Same emdashes, same filler, same hedging. I moved the banned word list into a Python scanner. Now every outbound draft hits two linters. They block emdashes, AI hype words, and 40-something other tells. The model can't talk its way past a regex. Track file dependencies with a graph. Canonical files reference each other. Change one and three others go stale. I keep a ripple-graph.json that maps these. When I edit talk-tracks, the system flags current-state and the engagement playbook for review. Chain sessions with handoffs and memory. (This is the big one) Sessions are drafts. The work is everything that survives the session: canonical files, memory, handoffs, output. If nothing persisted, you didn't work. You chatted. Every session in my system ends with /q-wrap. Writes a handoff doc, a memory update, and a status receipt. /q-morning reads all three before doing anything else. The handoff covers: what shipped, what's blocked, what's next, what I learned. Memory files hold the longer-term version. The result: I can sleep for a week, come back, and the system reminds me where I was, what I cared about, and what the next move is.Nothing about Claude Code does this by default. You build it. Cont

Elon Musk: will appeal to the Ninth Circuit.

X: "Regarding the OpenAI case, the judge & jury never actually ruled on the merits of the case, just on a calendar technicality. There is no question to anyone following the case in detail that Altman & Brockman did in fact enrich themselves by stealing a charity. The only question is WHEN they did it! I will be filing an appeal with the Ninth Circuit, because creating a precedent to loot charities is incredibly destructive to charitable giving in America. OpenAI was founded to benefit all of humanity." submitted by /u/Embarrassed-Slip8094 [link] [comments]

Fast mode now defaults to Opus 4.7 in Claude Code.

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

I built and shipped 3 products solo with Claude in 90 days. Here's everything I learned (no fluff)

Background: solo operator, no team, no funding, no co-founder. Just me and Claude. 90 days, 3 shipped products. Not a flex post. This is the unfiltered breakdown — what worked, what wasted weeks, and what I'd do differently. What worked: 1. Treating Claude like a senior engineer, not a chatbot. Stop asking "can you write code for X". Start with "here's the constraint, here's the trade-off I'm thinking, push back on my approach." The output quality jumped 3x the moment I stopped being polite. 2. CLAUDE.md is not optional. Wasted 2 weeks re-explaining my stack every session. One 80-line CLAUDE.md fixed it. If you're using Claude Code without this file you're paying a tax every prompt. 3. Subagents > sequential work. "Spin off a subagent to run the test suite while I keep building" was the unlock. Most solo devs aren't using parallel agents at all. They're leaving 40% of their throughput on the table. 4. Skills > prompts. Custom skill that auto-pulls docs based on which file I'm in. Setup took 4 hours. Pays off every single day. Stop copy-pasting context. 5. Sonnet for 80%, Opus for the gnarly 20%. Burning Opus tokens on Haiku-tier tasks was my dumbest mistake. Now I batch: Haiku for cleanups/summaries, Sonnet for building, Opus for architecture only. What didn't work: 6. Trying to "engineer the perfect prompt." If your prompt is generic, your output is generic. Skill issue. Just be specific about the constraint. 7. Building features I thought were cool. Shipped 2 features no user asked for. Both got 0 use. Now I refuse to code anything until a user has explicitly asked for it twice. 8. Hiring help. Tried to hire a contractor in week 6. Claude + me was already faster. Wasted $1,400 and 2 weeks of onboarding. Solo + Claude > Solo + Claude + slow human. The uncomfortable truth: Most "AI builders" on LinkedIn are content creators, not builders. They post screenshots of features they never shipped. The real builders are quiet. Heads down. Iterating. If you're shipping with Claude right now — solo or small team — drop what you're building below. Let's actually find each other. Not selling anything. Just trying to build a network of real builders, not the LinkedIn cosplay version. submitted by /u/Common_Software_8636 [link] [comments]

11 Claude things I wish someone had told me 12 months ago

Most "X tips" posts on this sub are surface level. here's the stuff that actually changed how I use claude after 18 months of daily use including 6 months in claude code. The Projects feature is doing more than you think. drop your codebase context, your style guide, your past PRs as project knowledge once. stop pasting the same context every chat. I wasted probably 100 hours before figuring this out. Custom Styles aren't a gimmick. I have one called "skeptical senior eng" that pushes back on my code instead of agreeing with everything. took 3 minutes to set up. single biggest output quality jump I've gotten. Memory is on by default now and it reads your past chats. if your responses suddenly feel weirdly personalized that's why. you can turn it off in settings. (freaked me out for like a week before I trusted it) Search past chats is hidden gold. I forget which chat had the working code. I just ask "what was the final auth setup we landed on last Tuesday" and it pulls it. saves me from scrolling. Sonnet 4.6 is faster than Opus 4.7 and 80% as good for most things. I default to Sonnet now and only switch to Opus for the gnarly architectural stuff. my limit complaints stopped. Haiku 4.5 is genuinely useful for batch work. need to clean 200 support tickets, draft 50 email replies, summarize 30 PDFs. Haiku. don't waste Opus tokens on Haiku tasks. The mobile voice mode is underrated for thinking out loud. I walk for 20 min, talk through a problem, then ask claude to summarize what I'm trying to figure out. solved more decisions on walks than in offsites. In claude code your CLAUDE.md is doing more work than the prompts. write 80 lines of project context once. stop re-explaining your stack every session. Skills > custom instructions for repetitive workflows. I have a skill that pulls the right docs based on what file I'm in. setup took an afternoon, pays off every day. Subagents in claude code unlock parallel work that mostly happens in your head. "spin off a subagent to run the test suite while I keep coding" is the move. most people don't use them at all. Artifacts can call the API now. you can build a working AI tool inside an artifact. people call it Claudeception. I made a client brief generator that calls Sonnet from inside an HTML artifact, took an hour. wild. if your claude output feels generic your prompt was generic. genuinely a skill issue. anyone got their own "took me way too long" list? drop yours below 👇 submitted by /u/No-Yogurtcloset4086 [link] [comments]

LLM-Rosetta — format conversion library across LLM API standards, doubles as a proxy

This started because we had a proprietary internal LLM API that spoke none of the standard formats. Built an internal conversion layer to bridge it, maintained that for over a year. As colleagues started adopting more and more coding tools — Claude Code, opencode, Codex, VS Code plugins, Goose, and whatever came out that week — each with its own API format expectations, maintaining separate adapters for each became the actual problem. That's what pushed the internal conversion layer into a proper generalized design, and llm-rosetta is the result. It's a Python library that converts between LLM API formats — OpenAI Chat, Responses/Open Responses, Anthropic, and Google GenAI. The idea is you convert through a shared IR so you don't end up writing N² adapters. The key difference from LiteLLM: LiteLLM is a unified calling layer that takes OpenAI-style input and transforms it into provider-native requests — one direction. llm-rosetta uses a hub-and-spoke IR, so each provider only needs one converter, and you get any-to-any conversion for free. Anthropic → Google, OpenAI Chat → Anthropic, whatever direction you need. Use it as a library — pip install and call convert() directly, no server needed. Or run the gateway if you want a proxy that handles the format translation for you. Zero required runtime dependencies either way. The HTTP server, client, and persistence layer are vendored from zerodep (https://github.com/Oaklight/zerodep), another project of mine — stdlib-only single-file modules, not someone else's library repackaged. The gateway ships with a Docker image if you'd rather not deal with Python env setup. You can also deploy it on HuggingFace Spaces or anything similar — admin panel, dashboard, request log, config management all included. Screenshots: https://llm-rosetta.readthedocs.io/en/latest/gateway/admin-panel/ We've been running it in production for about 5 months as the conversion layer for an internal multi-model access platform — needed to support various API standards and coding tool integrations before the upstream APIs were fully standardized. The Responses converter passes all 6 official Open Responses compliance tests (schema + semantic) from the spec repo. So if you're running Ollama, vLLM, or LM Studio with Responses endpoints, it should just work as one side of the conversion. There's a shim layer for provider-specific quirks — built-in shims for OpenRouter, DeepSeek, Qwen, xAI, Volcengine, etc. Converters stay generic per API standard, shims handle the edge cases declaratively. 24 cross-provider examples in the repo covering all provider pairs, SDK + REST, streaming, tool calls, image inputs, multi-turn with provider switching mid-conversation. GitHub: https://github.com/Oaklight/llm-rosetta Docs: https://llm-rosetta.readthedocs.io arXiv: https://arxiv.org/abs/2604.09360 Gateway screenshot: https://preview.redd.it/qzzjr2dcdw1h1.png?width=949&format=png&auto=webp&s=bce4293aae81059f794909fc37f85071cee34378 submitted by /u/Oaklight_dp [link] [comments]