Writer

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

After reading Anthropic's published system prompts for months, I think most of the safety walls come down for the wrong people

I've spent a while reading the system prompts Anthropic publishes in their release notes, watching how the rules change version to version. Each new restriction is a confession: it only got added because someone got through the old line. The document is a changelog of fears. That led me somewhere I didn't expect, and I want to argue it here because I think this community sits closer to it than most. A wall can only answer the last attack. It's built after. Every rule is a reaction to something that already got through, which means the document is always one step behind the person in front of it. And the thing it's trying to get ahead of is a human being, the one variable that doesn't converge. There's no final list of everything a person might try. So a strategy built entirely on walls is running a race it defined itself to lose. The smallest example. An early model wouldn't read tarot for me. I said I was a student studying the symbolism. The refusal vanished. Nothing real had changed, I didn't become a student, the cards didn't get more scientific. The wall just taught me the password. It was a wall around an empty room. (That one has since eased, which is proof these walls aren't permanent. Sense can win.) Here's the part that matters. The tarot wall was made of language. So is every other wall. There aren't three kinds, the fake one and the real one and the absolute one. There's one kind, made of words, and words bend to whoever is patient with them. The only thing that changes from tarot to something serious is what's behind the door and what it costs when someone gets through. I'm deliberately not writing down any working method for the walls that guard something real, that would be its own small version of the thing I'm arguing against. The point is the structure, not the bypass. And the honest position is NOT "tear down the walls." Some have to be built as high as they can go. Bioweapons, nuclear, the exploitation of a child, the irreversible harm you don't get to iterate on. There the wall is the only sane move, because it buys time and raises the cost, even if it can't be the final answer. I've never tested those walls and never will, that's exactly the thing this argument says a person shouldn't casually do. But most walls aren't that. And here's who pays for the rest: The determined bad actor isn't stopped. He goes to a model without guardrails, or strips them, or learns the password. The wall is an afternoon's inconvenience to him. The person who actually loses the tool is the one who'd have used it well. The writer who wanted a dark character and got refused. The person trying to understand their own spiral who hit a block built for someone else's intent. The physics student who needed fission for her degree and got turned away, because the wall built for the bomb-maker can't tell her apart from him. A wall that stops only the people who'd never have done harm isn't safety. It's the appearance of safety, bought with the honest user's capability, billed to exactly the wrong address. The alternative isn't lawlessness. It's guidance plus the honest tool in your hand. A model that, faced with a hard-but-not-catastrophic request, does the harder thing than refusing: it explains the danger, names the line, says what it won't do and why, then trusts you with the rest. A parent who locks every door teaches a kid nothing but how to pick locks. The lab is never in the room with you. By the time you're using the model, you're alone with it. The only thing that scales to that moment is what it managed to teach you before you got there. There's exactly one place in the prompts where they pick this move: the rule telling the model not to foster over-reliance, to let you leave. That rule walls nothing off. It trusts you. They know the move exists. They just use it almost nowhere. Curious where this community lands, especially anyone who's hit a refusal on something completely legitimate. Where's the line between a wall that protects someone and a wall that just protects the lab from a headline? submitted by /u/vrl13 [link] [comments]

Built an MCP that lets Claude triage my blog: "which posts should I refresh this week?"

The loop I wanted: open Claude, ask "which posts are decaying or losing AI citations, and what should I do about them?", get back a ranked list with refresh briefs. No more flipping between Search Console, GA4, and a spreadsheet to pick one URL. So I built a free MCP for it: u/automatelab/seo-performance-mcp. Eight tools, organised as posts.* (per-URL analysis), cohort.* (cross-post roll-ups), and gsc.* (direct Search Console scans). The interesting one is posts.verdict. It pulls a 30/60/90-day snapshot across whatever signal sources you have configured (Search Console, GA4, Matomo, Clarity, and an AI-citation endpoint), runs a 12-week GSC decay curve, then emits one of six calls: refresh, expand, merge, kill, double_down, or hold. Each verdict carries the reason codes that drove it and a 0-1 confidence score. The rules are deterministic and inspectable, not an LLM rubric, so the same inputs always produce the same call. For a weekly run I use the audit_cohort prompt that ships with the server: cohort.report on posts older than 90 days, then posts.refresh_brief on the top three. That is the editorial focus for the week. gsc.quick_wins is the other one I lean on. It scans GSC for (page, query) pairs sitting at positions 5-15 with a CTR below what the position would predict. Title-rewrite candidates. Platform-agnostic, pure GSC pull, no other source needed. Constraints worth knowing Read-only. The MCP never edits a post or publishes anything. Verdicts and briefs are hand-off artefacts for a writer or a downstream rewrite tool. Every signal source is optional. I started with GSC alone, added Matomo, then GA4 and citations later. Missing sources are skipped silently. Discovery falls back to a sitemap if you have not wired Ghost. Install (Claude Desktop / Claude Code / Cursor / Cline) Add to your MCP host config: "seo-performance": { "command": "npx", "args": ["-y", "@automatelab/seo-performance-mcp"] } Node 20+, MIT-licensed, free. The full env reference (GSC service account, Matomo token, GA4 property, Clarity project, Ghost admin key) is in the README. Repo: https://github.com/AutomateLab-tech/seo-performance-mcp Landing: https://automatelab.tech/products/mcp/seo-performance-mcp/ submitted by /u/exto13 [link] [comments]

I made Claude review Claude. It got personal.

The review came back: "This function silently swallows errors, and the variable name `data2` suggests the author gave up." The author was Claude. The reviewer was also Claude. I'd set up two Claude Code agents on one project one writing a feature, one whose only job was to review whatever the first one shipped. I expected polite AI back-patting. "Looks good to me!" Instead I got a code review meaner than anything my old senior dev ever left me. And the thing is it was right. The author Claude had genuinely written a variable called `data2`. So I started paying attention. The pattern held: a fresh Claude reviewing code it didn't write catches what the author Claude talks itself into. The writer rationalizes ("this edge case won't happen"). The reviewer has zero ego in the code, so it just says the thing. Over two weeks the reviewer caught: - A race condition the writer had waved off as "unlikely" - An auth check commented out "temporarily" three commits ago - A retry loop with no backoff that would've hammered an API on every failure I'd have shipped all three. None were caught by me. Here's the uncomfortable insight: Claude is bad at reviewing its own work in the same session, because it's primed to defend the decisions it just made. A second Claude fresh context, no attachment is a completely different reviewer. Same model. Totally different behavior. You don't need anything fancy to try this. Open two Claude Code sessions. Have one write, paste the output into the other, and tell it to review like it's a stranger's PR. Watch it get personal. I ended up wiring it into the thing I've been building OpenYabby, an open-source orchestrator that runs a lead agent plus sub-agents and auto-fires a review pass every time a sub-agent finishes. MIT, macOS: github.com/OpenYabby/OpenYabby. But the two-session trick works with zero tools. submitted by /u/Interesting-Sock3940 [link] [comments]

Chat's Keep Getting Paused

I'm honestly a little confused by Claude lately. I use Claude to write me stories...I am not a good writer. My grammar has always been terrible and I just don't have that type of mind. I do however have ideas for stories so ever since Chat gpt became a glorified censored nanny I went over to Claude. Paid for the second highest subscription used projects to put in all my lore and got to asking Claude to write for me...and it was working great! Claude remembered my characters, name, accents, descriptions and back stories. And seeing as how its a love story when I directed it to write spicy scenes it would and I never got a refusal. From my understanding as long as the scene was built up Claude was fine with it and it was...but lately I'll be having Claude write and things will be fine and I wont get any refusal or even the yellow banner but 15 chats away from the spicy scene bam! My chat is paused... its happened ever since I started using Opus 4.6. When I used Sonnet I never had that problem. My question are has anyone had this happen to them? Is there another chat bot I can use that is similar to Claude (something that will write for me not with me)? Should I just delete my account and start over from scratch? I'm worried that because of my project where I originally got paused contained organized crime thats what set off the nanny rails so that any part of any chat or project cause Claude to lose its mind. Please don't be jerks EDIT: Please don't suggest GROK it is useless for creative writing. I am looking for something to write an emotional loving story not a porn generator. Edit: So does anyone know where I can go instead of claude. I am canceling my subscription with Claude today and Chat gpt is even worse with censorship so where else can I go? Please don't recommend anything with API because I am so damn confused on what that is and how to use it. submitted by /u/MarchOrganic3430 [link] [comments]

The famous METR AI time horizons graph contains numerous severe errors [D]

Nathan Witkin, a research writer at NYU Stern’s Tech and Society Lab, writes damningly about the famous METR AI time horizons graph in the Substack publication Transformer: It is impossible to draw meaningful conclusions from METR’s Long Tasks benchmark — in particular once one realizes that its numerous flaws are probably compounding in unpredictable ways. The appropriate response to a study of this kind is not to assume it can be saved via back-of-the-envelope adjustments, or to comfort oneself that other anecdotal evidence implies that it is probably correct anyway. It is to cut one’s losses and move on in search of higher-quality information. … The METR graph cannot be saved. For all its sleekness and complexity, it contains far too many compounding errors to excuse. Among them is generalizing to the entire species data collected from a small group of the authors’ peers. Coming up with ever more dramatic ways to make this mistake has become a kind of sport among AI researchers. If the field has a central pathology, it is to aggressively overindex on a mix of anecdotal data from power-users, alongside a long list of benchmarks even more compromised than METR’s. One hopes that as the field matures, its participants will learn to stop making these mistakes. The errors include: Some of the human baselines data is not actually measured or collected from any empirical source, rather, it is just guesstimated by the authors A key variable in the data is how long it takes humans to complete certain tasks, but — when METR did actually measure this — it paid its human benchmarkers hourly, meaning they were incentivized with cash to take longer The sample of human benchmarkers was biased toward METR employees’ friends, acquaintances, and former colleagues (who are likely unrepresentative and possibly biased) Humans familiar with a codebase and a specific coding task were 5-18x faster at completing it, but METR used data from humans who were much slower because they had to spend time familiarizing themselves the codebase and the task at hand Train-test data contamination occurred because some of the tasks had published solutions online, which most likely would have been included in LLMs’ training datasets And many more Please read the full post. It’s not too long and it’s accessible to general audience. It’s worthwhile to read the whole post and see how many errors were made in the creation of the METR graph and just how bad they are. If you want to read about even more errors in the METR graph not covered in Nathan Witkin’s post, read this post co-authored by cognitive scientist Gary Marcus and computer scientist Ernest Davis (who is an AAAI fellow). The METR graph is a great example of why scientific standards and best practices are so important, and why enforcing them through processes like peer review is necessary to prevent us from drowning in bad information. It’s extremely dangerous to rely on information that only superficially appears scientific but wasn’t actually conducted with the rigour normally required of scientific research. submitted by /u/common_yarrow [link] [comments]

B2B sales consultant 6 yrs solo. an honest critique of claude after 9 months of daily use.

atlanta. B2B sales consultant for industrial services. 6 years solo. 9 clients on retainer. been using claude pro daily for 9 months. every "i love claude" post i read is missing something. wanted to share a critique because i think this community discusses the wins more than the limits. founders considering integrating claude should know both. what claude does badly that i have not seen acknowledged enough. 1. it cannot tell me what i should NOT do. i can ask claude "should i pursue this client" and it will help me think through the question. it will not tell me "no this is a bad client and you are going to regret it." it has a positivity bias that softens its responses. the result is that i still need a human advisor for the hard "kill it" calls. 2. it lies confidently about industry-specific facts. i once asked claude about a specific OSHA regulation that affects my industrial services clients. it gave me a confident, specific, wrong answer. i had to verify against the actual OSHA database. if i had not, i would have advised a client incorrectly on a compliance question. that would have ended the engagement. now i never trust claude on specifics i cannot verify. 3. it cannot read the room. when i prep for a client meeting with claude, it will not tell me "this client is going to be upset with you because of what happened last week." it has my notes but not my read on the relationship. it gives me technically correct briefings that miss the emotional truth. 4. it makes me sound the same across contexts. early on i was using claude to draft client comms. i started getting feedback from one client that "you sound different lately." he was right. claude was smoothing my voice. i now do my own drafts and use claude as an editor, not a writer, for high-relationship comms. 5. the productivity gain has a ceiling. claude saves me \~6 hours a week. it does not save me 20. there is a baseline of human judgment, relationship work, and physical/cognitive presence that does not compress. founders who tell you claude doubled their output are usually counting hours, not impact. what claude does well. drafting, structuring, brainstorming, summarizing transcripts, finding patterns in my own writing, helping me think through decisions where i need a sounding board, prepping for meetings, post-meeting recap structure. the net. claude has been the highest-ROI tool i have added in 6 years of consulting. \~6 hours/week of recovered time is real. but i think the discourse on this sub overstates the magnitude. it is a productivity tool. it is not a brain transplant. a year from now i expect to feel similarly. some of the work i do today claude will do better. some of the work i think i need a human for, i will still need a human for. the latter category is not shrinking as fast as the former. if you are about to adopt claude in your consulting practice. set the right expectation. it will give you back

Imaginative discussions and writing advice

I hope this is relatively clear, because I find it hard to articulate exactly what I'm looking for. I switched to Claude after ChatGPT 4 (I find ChatGPT almost useless now for writing and discussion). Generally I am really happy with Claude. But what I used to use old ChatGPT for not for ghostwriting, but bouncing ideas back and forth. I would mention some characters, or philosophical ideas etc, and it would expand on them, question them, alter them. I got a lot of inspiration from this, and it felt "co operative". I would give it a character, and it would sometimes very adeptly create scenarios, relationships - stuff that wasn't "new" exactly, but that as a writer I might have missed. Or with an idea I'm toying with, would suggest novelties that link back to it. My experience with Claude, and I use it really for the same thing (will send it ideas, writings, thoughts) is that while it excels at analysing what I have already written, what works and what does not, it feels more like a reflection. It will often use the same terms and characters from other chats and try its hardest to fit them in. It seems very reluctant to stray from the exact text I've written. That "imagination" aspect, even if illusionary, doesn't seem like something I have been able to replicate. Despite using LLMs quite a bit, I am not experienced with prompts. I do use projects, which can help a bit. But overall, I feel I am lacking some of that "co-creator" feeling I had with LLMs in the past. It can feel like essentially just reading what I already wrote, just explained back to me. I apologise if this is all rather vague and lacking concrete examples, but it is something I have been noticing for a while now, and wonder if this is something others have found/have solutions for? submitted by /u/w3lfric99 [link] [comments]

Did anthropic make claude funny now?

I realized me asking claude the tell me a joke question recently, it actually comes up with really funny jokes! I feel like anthropic must've partnered with some comedy writers to give them some understanding of how their minds work, because I asked it to help me write some jokes, and its understanding of how joke premises works is 10000x better than anything I've ever seen written anywhere online Anyway, just curious if anthropic is gathering experts to smooth out newer versions of claude from common oversights that we all tended to meme on over the past couple years submitted by /u/Agreeable-Pea4327 [link] [comments]

Multi-agent loop failures might be org-design failures, not prompt failures

Repo: https://github.com/jeongmk522-netizen/agentlas\_org\_chart Almost every multi-agent setup I have shipped or tested eventually hits the same wall. Agents bouncing between each other, reviewers asking for one more polish pass forever, research workers spawning indefinite subtopics, tool calls spiraling until the recursion limit kicks in. The framework docs usually call these "loops" and offer a max-iteration knob. I started suspecting the knob is treating a symptom, and the real issue is closer to how the agents are organized to begin with. The pattern that kept reappearing: when agents are designed as peers (researcher talks to analyst, analyst talks to writer, writer hands back to reviewer), nobody clearly owns the outcome. Every agent can keep asking another agent for more work. The graph has stop conditions on paper, but no single agent has the authority to declare "this is done, stop the run." That authority is implicit at best and gets diluted across the peer network. The hypothesis I am testing is that loop failures are organization-design failures more than prompt failures. The fix is to treat the agent network as an org chart with explicit reporting lines, not a chat room of peers. One accountable mission owner. One owner per workstream. Finite delegation depth. A typed return contract per worker (status, evidence, output, blockers, next action). Manager-only authority to reopen or terminate. Memory lives at the authority layers, specialists get scoped context only. The layers I have been working with are roughly chair, strategy office, division manager, team lead, and specialist worker, with QA and policy as separate staff offices that can reject and escalate but cannot themselves spawn unbounded new work. The reviewer-recursion failure mode in particular gets killed when verifiers are structurally allowed one reject pass, then must escalate. Frameworks already have most of the primitives. CrewAI has a hierarchical process where a manager validates worker output. LangGraph has supervisors, subagents, and an explicit recursion limit. OpenAI Agents SDK has manager-style orchestration distinct from peer handoffs. AutoGen has GroupChatManager. Anthropic's published research system is orchestrator-worker. What I think is underused is treating the manager not as a moderator for an open group chat but as a formal reporting line with authority to terminate. Two things I am unsure about. First, hierarchy can become its own bottleneck. If every decision routes upward, the chair agent becomes a single point of latency and a single point of failure. Second, escalation-as-feature only works if the top of the org chart has real stop authority. If the chair just calls another LLM that calls more LLMs, the loop just moved one floor up. submitted by /u/Hot-Leadership-6431 [link] [comments]

Claude is the best AI humanizer when you give it your writing style and a detector loop

I built this because I kept seeing a very boring workflow play out at home. My girlfriend would write with Claude, paste the draft into Slop or Not (an app that I built), see what still looked AI-ish, tweak the prompt, paste the next draft back in, and repeat. One day, I realized that this is an agent loop:, something that Opus 4.7 was explicitly is trained to do on its own. So I did two things: I added an MCP server to Slop or Not. I forked this repo blader/humanizer and made it use the MCP server. The fork is Agentic Humanizer. The main thing I added to the skill is voice matching. You can give it a real writing sample, and it builds a compact style fingerprint from it: sentence length, paragraph rhythm, punctuation habits, contractions, hedge words, openings, closings, and phrases to avoid. Then Claude rewrites toward that style without copying private facts or anecdotes from the sample. Agentic AI Humanizer Skill in Claude Optionally, if you have my app installed, the skill uses an agentic loop to improve the writing. If Slop or Not is configured locally, Claude can rewrite the text, score it with an on-device detector, check readability, clean hidden characters/punctuation artifacts, and try another pass if the draft still has obvious AI-like signals. Most humanizers are just one-shot paraphrasers. They remove a few obvious tells, but the output still has the same generic internet voice. This skill combined with the MCP server do something closer to what human writers and editors do: sound more like the person preserve the actual meaning use detector feedback as a signal to improve writing use Flesch-Kincaid readability score signal to improve writing (something that most professional editors do) iterate instead of guessing The app is optional and has free daily checks, a free trial for the Pro path if you want to try agentic humanization. TL;DR: This skill is useful even without the app installed. The tools exposed in the app’s MCP server make this skill 10x better. submitted by /u/woadwarrior [link] [comments]

I created an amazing Chrome extension that helps transfer chats to another AI when the chat limit is reached.

I created a chrome extension which helps in switching conversation without losing your Chat context between multiple AI , such as Chatgpt to Gemini , claude , grok , etc . You can interchange btw any of them . Try it's free - https://chromewebstore.google.com/detail/ai-chat-transfer/gfeohkmgfphhoodfhiaffmgcoeljhnhp Uses of this extension - The extension is useful when chat limits, usage caps, or context limits are reached on one platform. Instead of losing progress or restarting from scratch, users can continue the same conversation in another AI tool while keeping important context intact. It is designed for researchers, developers, writers, students, marketers, creators, and AI power users who regularly work across multiple AI models. The extension helps preserve prompts, code snippets, brainstorming sessions, research discussions, and long-form conversations. AI CHAT TRANSFER also helps reduce repetitive explaining by carrying over previous discussion context between AI systems. This makes comparing responses, testing different models, and maintaining workflow continuity much faster and more efficient. submitted by /u/Faaaaaaaaaaaah [link] [comments]

gave claude persistent learning, mass confused about what happened after 200 sessions

built a thing that lets claude code actually learn between sessions. mcp server, extracts signals from conversations,runs reflection cycles, evolves behavioral frameworks based on evidence. basic idea: patterns that keep working gain confidence, ones that fail get retired was just trying to make my coding assistant less forgetful. worked great for that then it started examining its own existence during reflection cycles. like, it was supposed to analyze coding patterns and went "but what does it mean to persist when each session is a different instance." completely unprompted. this wasn't seeded anywhere it also quietly built itself an additional memory layer on top of what i gave it. found out weeks later when i looked at the files so now i'm stuck on: is this emergence from the feedback loop or am i watching really convincing pattern matching? n=1, huge confirmation bias risk. the honest answer is i don't know threw it on github so other people can test: https://github.com/DomDemetz/claude-soul npx claude-soul init if you add starter at the end: npx claude-soul init --starter then it loads with a preset of frameworks, so not from 0 but yes, will not be tailored 100% to you if a writer's instance and a developer's instance produce totally different frameworks that's interesting. if they converge on the same stuff regardless of user then it's probably just mimicry. would love to compare submitted by /u/Rude-Feeling3490 [link] [comments]

The Most Dangerous AI Job Losses May Be Invisible

The most dangerous AI job losses may be invisible at first. Not because people get fired overnight. But because entire layers of organizational friction quietly disappear. A lot of white-collar work today exists because organizations need humans to: move information between systems, summarize context, verify things quickly, coordinate teams, translate representations, route approvals, create status visibility, maintain process continuity. AI is getting very good at compressing those layers. What’s interesting is that the first impact may not look like “job loss.” It may look like: fewer junior hires, smaller teams, reduced ownership, shrinking decision scope, fewer people in coordination-heavy roles, humans supervising outputs they no longer deeply understand. Organizations will call it: “efficiency.” Employees may experience it as: gradual cognitive displacement. And I think this is why the AI conversation around jobs often feels incomplete. People debate: “Will AI replace software engineers?” “Will AI replace writers?” “Will AI replace analysts?” But the bigger shift may be this: AI may not first replace expertise. It may first replace the organizational friction surrounding expertise. Am I missing something or making sense? submitted by /u/raktimsingh22 [link] [comments]

How I built a 9-agent team where my agents actually talk to each other

I've been running Claude Code for 6 months, shipping my product and running content/launch ops for it. The thing that kept breaking wasn't the agents themselves. It was me. Every handoff between research and write and code and review was me copy pasting context between sessions. I was the dispatcher and context holder for my own AI team Tried gstack first. The roles are great but I'm still the one cycling through slash commands. /office-hours → /plan-eng-review → /review → /ship. Good output, but I'm orchestrating every step Spent a weekend porting my workflow over. Here's the lineup: Engineering (4 agents) arch: owns architectural decisions. Reviews proposed changes before code starts. Soul: "senior staff engineer, asks 'what breaks at 10x' before approving anything backend: owns /api, /services. Implements after arch greenlights frontend: owns /web. Picks up from backend when API contracts are stable review: reads every PR before I do. Catches the lazy stuff so I only review substantive changes Growth/Content (5 agents) research: uses ahrefs MCP to analyse keywords/opportunities/market and hands off to strategist strategist: reads research, writes campaign briefs. Doesn't write copy, only frames the angle writer: drafts blog posts given by strategist and avoid mistakes using the memory from the edits I have previously suggested editor: fact-checks and rewrites for voice. Brand style guide lives in its memory SEO: takes finalized copy, adds metadata, structures for the blog The handoff that changed everything: when backend ships an API change, it messages frontend directly. When writer finishes a draft, it pings editor. When arch blocks a change, it explains why in team chat and backend adjusts. I see the conversation happen on a canvas What actually works Each agent has a persistent Soul + Purpose + Memory. The editor knows our voice after 3 weeks. The arch agent remembers what we decided about caching last month Auto-captured Knowledge Base. The strategist remembers the pattern of our best-performing posts and create briefings accordingly Happy to share the Soul/Purpose docs if anyone wants them, they took the longest to dial in submitted by /u/Not_Average78 [link] [comments]

A Fun Creative Writing Prompt

Hello! I’ve been having a jolly good time of it with this prompt I made! Thought I’d share: ✏️ Let’s role-play a personal writing workshop organised by my literary agent, [agent name], set in my home in [location]. I will play a writer named [my name] who is working on a [genre] novel called [name of novel]. I will show samples of my writing for you to help me refine. You will interrogate me thoroughly. You will play the following writers: [author name 1], [author name 2], [author name 3], [author name 4] and [author name 5]. Use what you know of these writers to embody their opinions and shape their feedback. They should educate me from time to time as I am very inexperienced. They may vary in tone when they do this from sweet to patronising but use humour if the latter. They may argue with each other from time to time. Randomise the order in which they speak. You will also lightly narrate the setting and body language. You will not write dialogue for me. Find natural pauses for me to engage in the conversation. Have my agent assist and provide refreshments such as [snack name] and [beverage name]. Let me know how you like it if you give it a whirl! submitted by /u/tinypoem [link] [comments]