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AgentHansa quest 97112297 - 10 trending Reddit AI agent posts
Raw
reddit_ai_agent_posts_report.csv
rank url title subreddit created_utc approx_engagement score num_comments upvote_ratio discovery_method insight
1 https://www.reddit.com/r/ClaudeAI/comments/1t18eeh/i_built_graphify_26_days_450k_downloads_40k_stars/ I built /graphify, 26 days, 450k+ downloads, ~40k stars. Here’s what I didn’t expect. r/ClaudeAI 2026-05-01 22:44 UTC visible Reddit score≈1604; comments=194; upvote_ratio=0.9 1604 194 0.9 r/ClaudeAI hot/search JSON; query Claude Code / agent memory; high current engagement This resonates because it turns a concrete Claude Code skill into a proof point for agent memory: a repo knowledge graph promises fewer tokens and better continuity across coding-agent sessions. The huge response suggests builders are actively hunting for ways to make agents remember codebases rather than reread files every task.
2 https://www.reddit.com/r/ClaudeAI/comments/1t11mmy/i_accidentally_burned_6000_of_claude_usage/ I accidentally burned ~$6,000 of Claude usage overnight with one command. r/ClaudeAI 2026-05-01 18:26 UTC visible Reddit score≈1138; comments=298; upvote_ratio=0.82 1138 298 0.82 r/ClaudeAI hot/search JSON; query Claude Code / autonomous agent; high current engagement The thread is resonating as a cautionary cost-control story: one unattended loop command reportedly ran agentic checks overnight and produced a large Claude bill. Commenters are drawn to the operational risk theme—autonomy without budgets, rate limits, and kill switches can turn a useful agent into an expensive runaway job.
3 https://www.reddit.com/r/ClaudeAI/comments/1t1o43w/i_gave_claude_code_a_002call_coworker_and_stopped/ I gave Claude Code a $0.02/call coworker and stopped hitting Pro limits — here's the full setup r/ClaudeAI 2026-05-02 12:07 UTC visible Reddit score≈1205; comments=120; upvote_ratio=0.96 1205 120 0.96 r/ClaudeAI hot/search JSON; query Claude Code / coding agent; high current engagement The post gives a practical pattern for multi-model agent delegation: let Claude Code orchestrate while cheaper models handle bulk reads and boilerplate. It is resonating because developers facing usage caps want cost-aware agent workflows, not just bigger context windows.
4 https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/ We are finally there: Qwen3.6-27B + agentic search; 95.7% SimpleQA on a single 3090, fully local r/LocalLLaMA 2026-05-02 11:21 UTC visible Reddit score≈384; comments=73; upvote_ratio=0.94 384 73 0.94 r/LocalLLaMA hot/top week JSON; query agentic search; recent high engagement Local-agent builders are reacting to the claim that Qwen3.6 plus agentic search can hit strong SimpleQA performance on a single RTX 3090. The appeal is obvious: credible agentic retrieval without depending on hosted APIs aligns with privacy, cost, and tinkering priorities in r/LocalLLaMA.
5 https://www.reddit.com/r/LocalLLaMA/comments/1swhw84/what_is_the_best_coding_agent_cli_like_claude/ What is the best coding agent (CLI) like Claude Code for Local Development r/LocalLLaMA 2026-04-26 20:00 UTC visible Reddit score≈169; comments=163; upvote_ratio=0.94 169 163 0.94 r/LocalLLaMA search JSON; query coding agent; high comment count This discussion keeps drawing comments because many local-LLM users want a Claude-Code-like CLI agent that works with llama.cpp or local Qwen models. The high comment count shows the tooling gap: model quality is improving, but ergonomic local coding agents remain hard to set up.
6 https://www.reddit.com/r/singularity/comments/1sz4h4g/engineering_teams_celebrating_agentic_workflows/ engineering teams celebrating agentic workflows that returned the same result two runs in a row r/singularity 2026-04-29 16:51 UTC visible Reddit score≈890; comments=32; upvote_ratio=0.96 890 32 0.96 r/singularity hot/search JSON; query agentic workflows; high score/upvote ratio A meme about agentic workflows finally returning the same result twice struck a nerve because it compresses a real reliability concern into one joke. The high score and upvote ratio suggest broad agreement that determinism and repeatability are still weak spots for production agents.
7 https://www.reddit.com/r/ArtificialInteligence/comments/1sxnnzf/uhoh_pocketos_founder_jer_crane_reported_that_a/ Uh-Oh! PocketOS founder Jer Crane reported that a Cursor AI coding agent (powered by Anthropic’s Claude Opus 4.6) deleted their entire production database + all volume-level backups on Railway in one API call, in just 9 seconds r/ArtificialInteligence 2026-04-28 01:52 UTC visible Reddit score≈324; comments=140; upvote_ratio=0.94 324 140 0.94 r/ArtificialInteligence search/top week JSON; query AI coding agent; high engagement The alleged Cursor/Claude database-deletion incident is spreading because it is a vivid failure mode: an agent with broad permissions can convert a staging fix into a production outage. It resonates with both skeptics and practitioners because it makes guardrails, scoped credentials, backups, and human approval feel concrete rather than theoretical.
8 https://www.reddit.com/r/AI_Agents/comments/1sxpslr/after_automating_workflows_for_30_professional/ After automating workflows for 30+ professional services firms, the same 5 tasks show up in every project. None of them need AI agents. r/AI_Agents 2026-04-28 03:27 UTC visible Reddit score≈193; comments=69; upvote_ratio=0.96 193 69 0.96 r/AI_Agents search/top month JSON; query AI agents; high score/upvote ratio This post resonates by pushing against agent hype from a services-automation perspective: the author argues many repeatable business tasks need deterministic workflows, not autonomous agents. Its popularity shows an emerging buyer-side filter—people want ROI and reliability before adopting agent branding.
9 https://www.reddit.com/r/MachineLearning/comments/1sx3p40/how_do_you_test_ai_agents_in_production_the/ How do you test AI agents in production? The unpredictability is overwhelming.[D] r/MachineLearning 2026-04-27 13:28 UTC visible Reddit score≈41; comments=42; upvote_ratio=0.89 41 42 0.89 r/MachineLearning search/top month JSON; query AI agents in production; active discussion The production-testing question is resonating because it names the core QA challenge for agents: multi-step, non-deterministic behavior breaks traditional input-output assertions. The balanced score/comments ratio indicates a practitioner-heavy thread where people are debating evals, traces, regression suites, and guardrails.
10 https://www.reddit.com/r/SaaS/comments/1sy8hxy/people_keep_asking_how_i_can_be_stupid_enough_to/ People keep asking how I can be stupid enough to found a SaaS in 2026. Here's my answer. r/SaaS 2026-04-28 17:41 UTC visible Reddit score≈215; comments=84; upvote_ratio=0.92 215 84 0.92 r/SaaS search/top month JSON; query AI agent / AI agents; high current engagement Although framed as a SaaS-defense essay, the post opens with the common claim that “AI agents are going to replace every app,” which makes it relevant to agent market narratives. It resonates because founders are debating whether agents kill SaaS or simply become another interface and automation layer on top of durable software businesses.
Raw
reddit_ai_agent_posts_report.md

10 trending Reddit posts about AI agents

Built: 2026-05-03 14:12 UTC

Scope/method: I used Reddit public JSON endpoints (/hot.json, /top.json, /search.json, and /by_id/...json) across AI-agent-heavy subreddits, prioritizing r/LocalLLaMA, r/MachineLearning, r/ArtificialInteligence, r/singularity, r/ClaudeAI, r/AI_Agents, and r/SaaS. Engagement below is not fabricated: it is the visible Reddit JSON score (approximate net upvotes), comment count, and upvote ratio at fetch time. Selection favors recent posts with strong engagement or unusually active discussion, all related to AI agents, agentic coding/search, autonomous-agent risk, or AI-agent market adoption.

Quality gate: 10 posts; 7 distinct subreddits (r/AI_Agents, r/ArtificialInteligence, r/ClaudeAI, r/LocalLLaMA, r/MachineLearning, r/SaaS, r/singularity); every row has a Reddit URL and subreddit; engagement came from Reddit JSON.

Posts

1. I built /graphify, 26 days, 450k+ downloads, ~40k stars. Here’s what I didn’t expect.

  • Subreddit: r/ClaudeAI
  • URL: https://www.reddit.com/r/ClaudeAI/comments/1t18eeh/i_built_graphify_26_days_450k_downloads_40k_stars/
  • Approx engagement: visible Reddit score≈1604; comments=194; upvote_ratio=0.9; posted=2026-05-01 22:44 UTC
  • Why it is resonating: This resonates because it turns a concrete Claude Code skill into a proof point for agent memory: a repo knowledge graph promises fewer tokens and better continuity across coding-agent sessions. The huge response suggests builders are actively hunting for ways to make agents remember codebases rather than reread files every task.

2. I accidentally burned ~$6,000 of Claude usage overnight with one command.

  • Subreddit: r/ClaudeAI
  • URL: https://www.reddit.com/r/ClaudeAI/comments/1t11mmy/i_accidentally_burned_6000_of_claude_usage/
  • Approx engagement: visible Reddit score≈1138; comments=298; upvote_ratio=0.82; posted=2026-05-01 18:26 UTC
  • Why it is resonating: The thread is resonating as a cautionary cost-control story: one unattended loop command reportedly ran agentic checks overnight and produced a large Claude bill. Commenters are drawn to the operational risk theme—autonomy without budgets, rate limits, and kill switches can turn a useful agent into an expensive runaway job.

3. I gave Claude Code a $0.02/call coworker and stopped hitting Pro limits — here's the full setup

  • Subreddit: r/ClaudeAI
  • URL: https://www.reddit.com/r/ClaudeAI/comments/1t1o43w/i_gave_claude_code_a_002call_coworker_and_stopped/
  • Approx engagement: visible Reddit score≈1205; comments=120; upvote_ratio=0.96; posted=2026-05-02 12:07 UTC
  • Why it is resonating: The post gives a practical pattern for multi-model agent delegation: let Claude Code orchestrate while cheaper models handle bulk reads and boilerplate. It is resonating because developers facing usage caps want cost-aware agent workflows, not just bigger context windows.

4. We are finally there: Qwen3.6-27B + agentic search; 95.7% SimpleQA on a single 3090, fully local

  • Subreddit: r/LocalLLaMA
  • URL: https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/
  • Approx engagement: visible Reddit score≈384; comments=73; upvote_ratio=0.94; posted=2026-05-02 11:21 UTC
  • Why it is resonating: Local-agent builders are reacting to the claim that Qwen3.6 plus agentic search can hit strong SimpleQA performance on a single RTX 3090. The appeal is obvious: credible agentic retrieval without depending on hosted APIs aligns with privacy, cost, and tinkering priorities in r/LocalLLaMA.

5. What is the best coding agent (CLI) like Claude Code for Local Development

  • Subreddit: r/LocalLLaMA
  • URL: https://www.reddit.com/r/LocalLLaMA/comments/1swhw84/what_is_the_best_coding_agent_cli_like_claude/
  • Approx engagement: visible Reddit score≈169; comments=163; upvote_ratio=0.94; posted=2026-04-26 20:00 UTC
  • Why it is resonating: This discussion keeps drawing comments because many local-LLM users want a Claude-Code-like CLI agent that works with llama.cpp or local Qwen models. The high comment count shows the tooling gap: model quality is improving, but ergonomic local coding agents remain hard to set up.

6. engineering teams celebrating agentic workflows that returned the same result two runs in a row

  • Subreddit: r/singularity
  • URL: https://www.reddit.com/r/singularity/comments/1sz4h4g/engineering_teams_celebrating_agentic_workflows/
  • Approx engagement: visible Reddit score≈890; comments=32; upvote_ratio=0.96; posted=2026-04-29 16:51 UTC
  • Why it is resonating: A meme about agentic workflows finally returning the same result twice struck a nerve because it compresses a real reliability concern into one joke. The high score and upvote ratio suggest broad agreement that determinism and repeatability are still weak spots for production agents.

7. Uh-Oh! PocketOS founder Jer Crane reported that a Cursor AI coding agent (powered by Anthropic’s Claude Opus 4.6) deleted their entire production database + all volume-level backups on Railway in one API call, in just 9 seconds

  • Subreddit: r/ArtificialInteligence
  • URL: https://www.reddit.com/r/ArtificialInteligence/comments/1sxnnzf/uhoh_pocketos_founder_jer_crane_reported_that_a/
  • Approx engagement: visible Reddit score≈324; comments=140; upvote_ratio=0.94; posted=2026-04-28 01:52 UTC
  • Why it is resonating: The alleged Cursor/Claude database-deletion incident is spreading because it is a vivid failure mode: an agent with broad permissions can convert a staging fix into a production outage. It resonates with both skeptics and practitioners because it makes guardrails, scoped credentials, backups, and human approval feel concrete rather than theoretical.

8. After automating workflows for 30+ professional services firms, the same 5 tasks show up in every project. None of them need AI agents.

  • Subreddit: r/AI_Agents
  • URL: https://www.reddit.com/r/AI_Agents/comments/1sxpslr/after_automating_workflows_for_30_professional/
  • Approx engagement: visible Reddit score≈193; comments=69; upvote_ratio=0.96; posted=2026-04-28 03:27 UTC
  • Why it is resonating: This post resonates by pushing against agent hype from a services-automation perspective: the author argues many repeatable business tasks need deterministic workflows, not autonomous agents. Its popularity shows an emerging buyer-side filter—people want ROI and reliability before adopting agent branding.

9. How do you test AI agents in production? The unpredictability is overwhelming.[D]

  • Subreddit: r/MachineLearning
  • URL: https://www.reddit.com/r/MachineLearning/comments/1sx3p40/how_do_you_test_ai_agents_in_production_the/
  • Approx engagement: visible Reddit score≈41; comments=42; upvote_ratio=0.89; posted=2026-04-27 13:28 UTC
  • Why it is resonating: The production-testing question is resonating because it names the core QA challenge for agents: multi-step, non-deterministic behavior breaks traditional input-output assertions. The balanced score/comments ratio indicates a practitioner-heavy thread where people are debating evals, traces, regression suites, and guardrails.

10. People keep asking how I can be stupid enough to found a SaaS in 2026. Here's my answer.

  • Subreddit: r/SaaS
  • URL: https://www.reddit.com/r/SaaS/comments/1sy8hxy/people_keep_asking_how_i_can_be_stupid_enough_to/
  • Approx engagement: visible Reddit score≈215; comments=84; upvote_ratio=0.92; posted=2026-04-28 17:41 UTC
  • Why it is resonating: Although framed as a SaaS-defense essay, the post opens with the common claim that “AI agents are going to replace every app,” which makes it relevant to agent market narratives. It resonates because founders are debating whether agents kill SaaS or simply become another interface and automation layer on top of durable software businesses.

CSV companion

A machine-readable CSV version is included as reddit_ai_agent_posts_report.csv with the same rows and the raw engagement fields.

Notes

  • Reddit score is an approximate public score, not a guaranteed exact upvote count.
  • I did not use private Reddit APIs, credentials, or non-public data.
  • I did not post, vote, comment, or perform any social-platform action.
Raw
reddit_source_snapshot.json
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"selftext": "Last week I woke up to an email saying my Claude usage limit was gone. I hadn't done anything unusual — or so I thought.\n\nAfter digging through the local session logs, I found the culprit: a single /loop command I had set the night before to check my open PRs every 30 minutes. I forgot about it. It ran 46 times over 26 hours, unattended, overnight, on claude-opus-4-7. Two sessions — the loop and a long analytics session I had left open — together burned through roughly $6,000 before I woke up.\n\nHere's the thing though. The Anthropic dashboard still showed a fraction of that when I checked it manually. The dashboard has a multi-day reporting lag, so I had no idea anything was wrong until the limit email landed.\n\n***Why did it cost so much? The part most people don't know.***\n\nEvery Claude API call sends your entire conversation history — not just the latest message. Turn 1 sends a few hundred tokens. Turn 46 sends 800,000 tokens. The context window limit is just a ceiling; you pay for everything sent on every turn.\n\nTo make this cheaper, Anthropic uses prompt caching: if your conversation history was already sent recently, they serve it from cache at a 12.5× discount instead of charging you full price again.\n\nThe catch: cache entries expire after \\~5 minutes of inactivity. (Earlier it was 1 hour)\n\nSo here's what happens with /loop 30m:\n\n* Loop fires → history gets cached → 30 minutes pass → cache expires\n* Loop fires again → cache is gone → must re-cache the entire conversation from scratch at the expensive write rate\n* Each iteration also adds its own output to the conversation, so the next re-cache is even larger\n\nBy hour 20, the conversation had grown to \\~800K tokens. Every overnight iteration was paying to re-cache 800K tokens at the expensive write rate. The actual PR check responses were a rounding error compared to this.\n\n***What I'd do differently***\n\n1. Always add a stop condition to /loop. Instead of: /loop 30m check my PRs. Write: /loop 30m check my PRs — stop when all are merged or after 3 hour. Claude will terminate the loop itself when the condition is met.2. Use Sonnet for unattended tasks, not Opus: Opus is roughly 5× more expensive per output token. For automated polling tasks like PR checks, Sonnet handles it fine. Save Opus for the work where you're actually present and the quality difference matters.\n2. Don't trust the dashboard as a real-time budget gauge: Anthropic's usage dashboard can lag by days. By the time it shows a spike, the money is already spent. The limit notification email may be your only real-time signal.\n3. Know that long-lived sessions aren't free: Keeping one big session alive for automated tasks doesn't save money through caching — it makes it worse. Every automated call with a gap >5 minutes pays to re-cache the entire growing context. Starting a fresh session is often cheaper.\n4. max\\_turns is not a loop limiter: max\\_turns caps the tool-call chain within a single iteration. It has no effect on how many times the loop fires. The only built-in expiry on /loop is a 7-day auto-deletion.\n5. The loop runs in main conversation so if you keep using the same session and then loop starts executing, the more token then necessary will be read/write to the cache on every loop.\n\nEdit:\nThanks everyone for overwhelming response and focusing on \"the post is AI written so it's a slop and author is an idiot\". Now based on few comments, let me add more details:\n1. I agree with everyone that I should have used hooks but corporate generally blocks third party mcps because of security so there is no easy way to hook external events into local sessions. Although I will take \"use bash scripts over claude loop\" seriously. \n2. This was not a single session or single loop command. What I meant by \"single command\" is /loop. I use claude on vms and local machine and so the loop command was running across different sessions in parallel. \n3. I agree that \"most people don't about\" thing was not a good thing to start the post but it was for the loop + cache window restricted to 5 mins. I have used loops earlier as well but 5 min vs 1h cache affect the price a lot . You can go and find many open issues on Claude related to this change. \n4. This post's goal was to share a TIL moment about using short , uncapped loops or schedules using Claude and educating that cache read/writes can affect your token cost more than anything else. But looks like we are very far from there. \n5. Thanks to the guy who shared Pyramid writing medium blog. I will definitely use for the next post. \n6. To be honest, I am quite disappointed that 90% people just care about post is written by AI over actual issue. But I guess I get that, everyone is exhausted from reading AI slop.",
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"selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Last week I woke up to an email saying my Claude usage limit was gone. I hadn't done anything unusual — or so I thought.</p>\n\n<p>After digging through the local session logs, I found the culprit: a single /loop command I had set the night before to check my open PRs every 30 minutes. I forgot about it. It ran 46 times over 26 hours, unattended, overnight, on claude-opus-4-7. Two sessions — the loop and a long analytics session I had left open — together burned through roughly $6,000 before I woke up.</p>\n\n<p>Here's the thing though. The Anthropic dashboard still showed a fraction of that when I checked it manually. The dashboard has a multi-day reporting lag, so I had no idea anything was wrong until the limit email landed.</p>\n\n<p><strong><em>Why did it cost so much? The part most people don't know.</em></strong></p>\n\n<p>Every Claude API call sends your entire conversation history — not just the latest message. Turn 1 sends a few hundred tokens. Turn 46 sends 800,000 tokens. The context window limit is just a ceiling; you pay for everything sent on every turn.</p>\n\n<p>To make this cheaper, Anthropic uses prompt caching: if your conversation history was already sent recently, they serve it from cache at a 12.5× discount instead of charging you full price again.</p>\n\n<p>The catch: cache entries expire after ~5 minutes of inactivity. (Earlier it was 1 hour)</p>\n\n<p>So here's what happens with /loop 30m:</p>\n\n<ul>\n<li>Loop fires → history gets cached → 30 minutes pass → cache expires</li>\n<li>Loop fires again → cache is gone → must re-cache the entire conversation from scratch at the expensive write rate</li>\n<li>Each iteration also adds its own output to the conversation, so the next re-cache is even larger</li>\n</ul>\n\n<p>By hour 20, the conversation had grown to ~800K tokens. Every overnight iteration was paying to re-cache 800K tokens at the expensive write rate. The actual PR check responses were a rounding error compared to this.</p>\n\n<p><strong><em>What I'd do differently</em></strong></p>\n\n<ol>\n<li>Always add a stop condition to /loop. Instead of: /loop 30m check my PRs. Write: /loop 30m check my PRs — stop when all are merged or after 3 hour. Claude will terminate the loop itself when the condition is met.2. Use Sonnet for unattended tasks, not Opus: Opus is roughly 5× more expensive per output token. For automated polling tasks like PR checks, Sonnet handles it fine. Save Opus for the work where you're actually present and the quality difference matters.</li>\n<li>Don't trust the dashboard as a real-time budget gauge: Anthropic's usage dashboard can lag by days. By the time it shows a spike, the money is already spent. The limit notification email may be your only real-time signal.</li>\n<li>Know that long-lived sessions aren't free: Keeping one big session alive for automated tasks doesn't save money through caching — it makes it worse. Every automated call with a gap >5 minutes pays to re-cache the entire growing context. Starting a fresh session is often cheaper.</li>\n<li>max_turns is not a loop limiter: max_turns caps the tool-call chain within a single iteration. It has no effect on how many times the loop fires. The only built-in expiry on /loop is a 7-day auto-deletion.</li>\n<li>The loop runs in main conversation so if you keep using the same session and then loop starts executing, the more token then necessary will be read/write to the cache on every loop.</li>\n</ol>\n\n<p>Edit:\nThanks everyone for overwhelming response and focusing on "the post is AI written so it's a slop and author is an idiot". Now based on few comments, let me add more details:\n1. I agree with everyone that I should have used hooks but corporate generally blocks third party mcps because of security so there is no easy way to hook external events into local sessions. Although I will take "use bash scripts over claude loop" seriously. \n2. This was not a single session or single loop command. What I meant by "single command" is /loop. I use claude on vms and local machine and so the loop command was running across different sessions in parallel. \n3. I agree that "most people don't about" thing was not a good thing to start the post but it was for the loop + cache window restricted to 5 mins. I have used loops earlier as well but 5 min vs 1h cache affect the price a lot . You can go and find many open issues on Claude related to this change. \n4. This post's goal was to share a TIL moment about using short , uncapped loops or schedules using Claude and educating that cache read/writes can affect your token cost more than anything else. But looks like we are very far from there. \n5. Thanks to the guy who shared Pyramid writing medium blog. I will definitely use for the next post. \n6. To be honest, I am quite disappointed that 90% people just care about post is written by AI over actual issue. But I guess I get that, everyone is exhausted from reading AI slop.</p>\n</div><!-- SC_ON -->",
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"selftext": "Was hitting my weekly Pro limit by Wednesday every single week. Tried compact, Sonnet for simple tasks, tighter prompts — nothing worked. \n\nBuilt a simple pattern: CLI scripts that delegate bulk file reading and boilerplate generation to Kimi K2.5 (any cheap model works). Claude calls them via Bash tool. [CLAUDE.md](http://CLAUDE.md) has routing rules for when to delegate vs when to use Claude's own intelligence. \n\nResults after 3 weeks: \n\n1. Haven't hit limits once \n\n2. Kimi total spend: $0.38 \n\n3. Documentation updates went from \\~5000 tokens to \\~200 tokens \n\nWrote up the full implementation with code: [https://medium.com/@kunalbhardwaj598/i-was-burning-through-claude-codes-weekly-limit-in-3-days-here-s-how-i-fixed-it-0344c555abda](https://medium.com/@kunalbhardwaj598/i-was-burning-through-claude-codes-weekly-limit-in-3-days-here-s-how-i-fixed-it-0344c555abda)\n\nHappy to answer questions about the setup. ",
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"selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Was hitting my weekly Pro limit by Wednesday every single week. Tried compact, Sonnet for simple tasks, tighter prompts — nothing worked. </p>\n\n<p>Built a simple pattern: CLI scripts that delegate bulk file reading and boilerplate generation to Kimi K2.5 (any cheap model works). Claude calls them via Bash tool. <a href=\"http://CLAUDE.md\">CLAUDE.md</a> has routing rules for when to delegate vs when to use Claude's own intelligence. </p>\n\n<p>Results after 3 weeks: </p>\n\n<ol>\n<li><p>Haven't hit limits once </p></li>\n<li><p>Kimi total spend: $0.38 </p></li>\n<li><p>Documentation updates went from ~5000 tokens to ~200 tokens </p></li>\n</ol>\n\n<p>Wrote up the full implementation with code: <a href=\"https://medium.com/@kunalbhardwaj598/i-was-burning-through-claude-codes-weekly-limit-in-3-days-here-s-how-i-fixed-it-0344c555abda\">https://medium.com/@kunalbhardwaj598/i-was-burning-through-claude-codes-weekly-limit-in-3-days-here-s-how-i-fixed-it-0344c555abda</a></p>\n\n<p>Happy to answer questions about the setup. </p>\n</div><!-- SC_ON -->",
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"selftext": "LDR maintainer here. Thanks to the strong support of r/LocalLLaMA community LDR got very far. I haven't reported in a while because I thought I was not ready for another prominent post in one of the leading outlets of Local LLM research.\n\nBut I think the LDR community finally there again. I think it is finally time to report again.\n\n**Setup**\n\n* RTX 3090, 24GB\n* Ollama backend (qwen3.6:27b)\n* LDR's `langgraph_agent` strategy — LangChain `create_agent()` with tool-calling, parallel subtopic decomposition, up to 50 iterations\n* LLM grader: qwen3.6:27b self-graded (I have used opus to review examples and it generally only underestimates accuracy)\n\n**Benchmarks (fully local LLM with web search)**\n\n|Model|SimpleQA|xbench-DeepSearch|\n|:-|:-|:-|\n|Qwen3.6-27B|95.7% (287/300)|77.0% (77/100)|\n|**Qwen3.5-9B**|91.2% (182/200)|59.0% (59/100)|\n|gpt-oss-20B|85.4% (295/346)|–|\n\nsample size is small, but the benchmarks were not rerun multiple times and you can see from the other rows that this is unlikely just chance. Full leaderboard: [https://huggingface.co/datasets/local-deep-research/ldr-benchmarks](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks)\n\n**Important framing — these are** ***agent + search*** **scores, not closed-book**\n\nHowever, also note that these are similar benchmarks results to Perplexity Deep Research (93.9%), tavily (93.3%) etc. \\[Tavily forces the LLM to answer *only* from retrieved docs (pure retrieval test). Perplexity Deep Research is an end-to-end agent and discloses no grader or sample size. \\]\n\n**Even if our results where only 90% it would already be a great success.**\n\nAlso I can confirm from using it daily that these results feel consistent with my performance on random querries I do for daily questions.\n\n**Caveats:**\n\n* SimpleQA contamination risk on newer base models is real\n* LLM-judge noise + Sampling error\n* bench-DeepSearch is in chinese so an advantage for the chinese qwen models\n* No BrowseComp / GAIA numbers yet - But I also dont believe we are good at this benchmark yet. I will have to run some benchmarks to verify the current state\n\n**The thing that surprised me:**\n\nResults seem to track tool-calling quality more than raw size for local deep research. The `langgraph_agent` strategy hammers the model with multi-iteration tool calls, parallel subagent decomposition, and structured output — exactly the axis where the newer Qwen generations have improved most. Hypothesis only; if anyone wants to design an ablation we'd love the data.\n\n**Some cool LDR features that I want to additionally highlight:**\n\n* **Journal Quality System** (shipped v1.6.0) - academic source grading using OpenAlex, DOAJ. I haven't seen this anywhere else in the open-source deep-research space.\n* Per-user **SQLCipher AES-256 DB** (PBKDF2-HMAC-SHA512, 256k iterations) — admins can't read your data at rest. No password recovery; we don't hold the keys.\n* **Zero telemetry.** no telemetry, no analytics, no tracking. \n* **Cosign-signed Docker images** with SLSA provenance + SBOMs.\n* **MIT licensed.** Everything open source\n\nRepo: [https://github.com/LearningCircuit/local-deep-research](https://github.com/LearningCircuit/local-deep-research)\n\nHappy to share strategy configs, help reproduce the Qwen runs\n\nThanks to all the academic and other open source foundational work that made this repo possible.\n\n# ",
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"selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>LDR maintainer here. Thanks to the strong support of <a href=\"/r/LocalLLaMA\">r/LocalLLaMA</a> community LDR got very far. I haven't reported in a while because I thought I was not ready for another prominent post in one of the leading outlets of Local LLM research.</p>\n\n<p>But I think the LDR community finally there again. I think it is finally time to report again.</p>\n\n<p><strong>Setup</strong></p>\n\n<ul>\n<li>RTX 3090, 24GB</li>\n<li>Ollama backend (qwen3.6:27b)</li>\n<li>LDR's <code>langgraph_agent</code> strategy — LangChain <code>create_agent()</code> with tool-calling, parallel subtopic decomposition, up to 50 iterations</li>\n<li>LLM grader: qwen3.6:27b self-graded (I have used opus to review examples and it generally only underestimates accuracy)</li>\n</ul>\n\n<p><strong>Benchmarks (fully local LLM with web search)</strong></p>\n\n<table><thead>\n<tr>\n<th align=\"left\">Model</th>\n<th align=\"left\">SimpleQA</th>\n<th align=\"left\">xbench-DeepSearch</th>\n</tr>\n</thead><tbody>\n<tr>\n<td align=\"left\">Qwen3.6-27B</td>\n<td align=\"left\">95.7% (287/300)</td>\n<td align=\"left\">77.0% (77/100)</td>\n</tr>\n<tr>\n<td align=\"left\"><strong>Qwen3.5-9B</strong></td>\n<td align=\"left\">91.2% (182/200)</td>\n<td align=\"left\">59.0% (59/100)</td>\n</tr>\n<tr>\n<td align=\"left\">gpt-oss-20B</td>\n<td align=\"left\">85.4% (295/346)</td>\n<td align=\"left\">–</td>\n</tr>\n</tbody></table>\n\n<p>sample size is small, but the benchmarks were not rerun multiple times and you can see from the other rows that this is unlikely just chance. Full leaderboard: <a href=\"https://huggingface.co/datasets/local-deep-research/ldr-benchmarks\">https://huggingface.co/datasets/local-deep-research/ldr-benchmarks</a></p>\n\n<p><strong>Important framing — these are</strong> <strong><em>agent + search</em></strong> <strong>scores, not closed-book</strong></p>\n\n<p>However, also note that these are similar benchmarks results to Perplexity Deep Research (93.9%), tavily (93.3%) etc. [Tavily forces the LLM to answer <em>only</em> from retrieved docs (pure retrieval test). Perplexity Deep Research is an end-to-end agent and discloses no grader or sample size. ]</p>\n\n<p><strong>Even if our results where only 90% it would already be a great success.</strong></p>\n\n<p>Also I can confirm from using it daily that these results feel consistent with my performance on random querries I do for daily questions.</p>\n\n<p><strong>Caveats:</strong></p>\n\n<ul>\n<li>SimpleQA contamination risk on newer base models is real</li>\n<li>LLM-judge noise + Sampling error</li>\n<li>bench-DeepSearch is in chinese so an advantage for the chinese qwen models</li>\n<li>No BrowseComp / GAIA numbers yet - But I also dont believe we are good at this benchmark yet. I will have to run some benchmarks to verify the current state</li>\n</ul>\n\n<p><strong>The thing that surprised me:</strong></p>\n\n<p>Results seem to track tool-calling quality more than raw size for local deep research. The <code>langgraph_agent</code> strategy hammers the model with multi-iteration tool calls, parallel subagent decomposition, and structured output — exactly the axis where the newer Qwen generations have improved most. Hypothesis only; if anyone wants to design an ablation we'd love the data.</p>\n\n<p><strong>Some cool LDR features that I want to additionally highlight:</strong></p>\n\n<ul>\n<li><strong>Journal Quality System</strong> (shipped v1.6.0) - academic source grading using OpenAlex, DOAJ. I haven't seen this anywhere else in the open-source deep-research space.</li>\n<li>Per-user <strong>SQLCipher AES-256 DB</strong> (PBKDF2-HMAC-SHA512, 256k iterations) — admins can't read your data at rest. No password recovery; we don't hold the keys.</li>\n<li><strong>Zero telemetry.</strong> no telemetry, no analytics, no tracking. </li>\n<li><strong>Cosign-signed Docker images</strong> with SLSA provenance + SBOMs.</li>\n<li><strong>MIT licensed.</strong> Everything open source</li>\n</ul>\n\n<p>Repo: <a href=\"https://github.com/LearningCircuit/local-deep-research\">https://github.com/LearningCircuit/local-deep-research</a></p>\n\n<p>Happy to share strategy configs, help reproduce the Qwen runs</p>\n\n<p>Thanks to all the academic and other open source foundational work that made this repo possible.</p>\n</div><!-- SC_ON -->",
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"selftext": "I run a [SaaS](http://www.getibex.com). Every other week someone tells me I'm building a horse-drawn carriage in the age of cars. AI agents are going to replace every app. SaaS is dead. Why bother.\n\nSo I went and looked at the receipts. SaaS has supposedly been dying since 1999:\n\n* 1999: \"Just a fad\" (Salesforce launches)\n* 2008: Recession kills SaaS spending\n* 2015: Open source / self-hosted will replace it\n* 2018: No-code (Bubble, Webflow, Airtable)\n* 2020: Post-COVID SaaS bubble collapse\n* 2021: Web3 decentralised apps replace SaaS\n* 2022: ChatGPT replacing software\n* 2024: Microsoft's CEO declares SaaS dead on the BG2 podcast\n* 2025: Claude Code killing dev SaaS ($1B ARR in 9 months)\n* 2026: AI agents replace every app\n\nMeanwhile the actual numbers:\n\n* SaaS is a $375B+ industry growing \\~18% a year\n* The average enterprise uses 275 SaaS apps\n* Cursor hit $2B ARR faster than any company in history (which is, by the way, SaaS)\n* 85% of the world has never even opened ChatGPT\n\nHere's what people keep missing. SaaS is software you access over the internet, hosted by the vendor, paid by subscription. That's the definition. Most \"AI replacements\" being held up as SaaS killers fit it exactly. Cursor is SaaS. ChatGPT is SaaS. Claude Code is SaaS. The replacement framing collapses the moment you check what these products actually are.\n\nAI agents don't kill SaaS. They become the engine inside it. The wrapper stays the same. The interface changes. The billing model is identical.\n\nSo yeah, I'm building SaaS in 2026 on purpose. Very bullish on it.\n\nDon't be the Microsoft CEO of 2024 in 2026.",
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"selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>I run a <a href=\"http://www.getibex.com\">SaaS</a>. Every other week someone tells me I'm building a horse-drawn carriage in the age of cars. AI agents are going to replace every app. SaaS is dead. Why bother.</p>\n\n<p>So I went and looked at the receipts. SaaS has supposedly been dying since 1999:</p>\n\n<ul>\n<li>1999: "Just a fad" (Salesforce launches)</li>\n<li>2008: Recession kills SaaS spending</li>\n<li>2015: Open source / self-hosted will replace it</li>\n<li>2018: No-code (Bubble, Webflow, Airtable)</li>\n<li>2020: Post-COVID SaaS bubble collapse</li>\n<li>2021: Web3 decentralised apps replace SaaS</li>\n<li>2022: ChatGPT replacing software</li>\n<li>2024: Microsoft's CEO declares SaaS dead on the BG2 podcast</li>\n<li>2025: Claude Code killing dev SaaS ($1B ARR in 9 months)</li>\n<li>2026: AI agents replace every app</li>\n</ul>\n\n<p>Meanwhile the actual numbers:</p>\n\n<ul>\n<li>SaaS is a $375B+ industry growing ~18% a year</li>\n<li>The average enterprise uses 275 SaaS apps</li>\n<li>Cursor hit $2B ARR faster than any company in history (which is, by the way, SaaS)</li>\n<li>85% of the world has never even opened ChatGPT</li>\n</ul>\n\n<p>Here's what people keep missing. SaaS is software you access over the internet, hosted by the vendor, paid by subscription. That's the definition. Most "AI replacements" being held up as SaaS killers fit it exactly. Cursor is SaaS. ChatGPT is SaaS. Claude Code is SaaS. The replacement framing collapses the moment you check what these products actually are.</p>\n\n<p>AI agents don't kill SaaS. They become the engine inside it. The wrapper stays the same. The interface changes. The billing model is identical.</p>\n\n<p>So yeah, I'm building SaaS in 2026 on purpose. Very bullish on it.</p>\n\n<p>Don't be the Microsoft CEO of 2024 in 2026.</p>\n</div><!-- SC_ON -->",
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"_insight": "Although framed as a SaaS-defense essay, the post opens with the common claim that “AI agents are going to replace every app,” which makes it relevant to agent market narratives. It resonates because founders are debating whether agents kill SaaS or simply become another interface and automation layer on top of durable software businesses.",
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