Reddit AI - 2026-07-05¶
1. What People Are Talking About¶
1.1 Local open models are getting more specialized and more benchmarked (🡕)¶
The strongest local-model conversation was not about a single chatbot release. It was about specialized artifacts, workload-specific benchmarks, and whether open-weight models can keep matching frontier coding tools. At least five substantial threads supported this theme: Google’s TabFM release, LongCat 2.0 open weights, a long-context agent benchmark, a fantasy RP benchmark, and a thread asking for a real Qwen 3.7 9B successor.
Balance- surfaced Google’s TabFM, which the Google Research blog and Hugging Face release describe as a zero-shot tabular model for classification and regression without task-specific fine-tuning or hyperparameter search (post) (520 points, 94 comments). The most useful reply came from u/SMFet (score 52), who said their lab’s real-data tabular models still beat public TFMs on privacy-sensitive corporate data, shifting the discussion from novelty to transferability.
Nunki08 shared LongCat 2.0 open weights under MIT; the Hugging Face page describes a 1.6T-parameter MoE model with about 48B active parameters per token, hundreds of billions of 1M-context training tokens, and support for Claude Code, OpenClaw, and Hermes (post) (322 points, 95 comments). The reviewed images mattered because they added two facts the title did not: one chart positioned LongCat against Gemini, GPT-5.5, and Opus variants on coding-agent benchmarks, while another image made the deployment footprint concrete at 3.55 TB in BF16.
linuxid10t posted a long-context local benchmark arguing that at 65K-128K context, prefill dominates wall-clock time and KV-head count matters more than parameter count for agentic workloads (post) (263 points, 106 comments). The thread was useful because it also attracted methodological pushback: u/OkFly3388 (score 22) said a 65K-in, 300-token-out workload was not representative of many Hermes sessions, while u/xornullvoid (score 21) argued typical agentic coding starts nearer 20K-25K context and grows over time.
UsedMorning9886 ran a fantasy RP and agentic benchmark where gemma-4-31B led at 87%, Qwen3.6-27B followed at 82%, and gemma-4-12B hit 80% (post) (93 points, 47 comments). The headline chart got attention, but u/SomeoneSimple (score 52) immediately pointed out that the post talked about category-level failures while only publishing the overall pass-rate graphic.
HitarthSurana made the unmet need explicit by asking whether any 8B-9B local model now beats Qwen 3.5 9B, and whether Alibaba will ever ship a Qwen 3.7 9B open-weight release (post) (65 points, 38 comments). The top replies said there is still no roadmap and that Gemma 4 12B is the nearest practical alternative above that size class.
Discussion insight: The recurring disagreement was not “open vs closed” in the abstract. It was whether public open-weight results actually transfer to real datasets, real coding sessions, and real hardware constraints. That kept even celebratory release threads grounded in evaluation details and deployment cost.
Comparison to prior day: This theme was already strong on July 4, when LongCat 2 weights drew 174 points and 37 comments and a GLM5.2-on-Pro-6000s hardware thread drew 1,120 points and 366 comments (LongCat post; hardware post). On July 5 the focus moved from “can I run this?” toward released weights, benchmark methodology, and small-model gaps.
1.2 Coding agents are getting real software results, but trust in them is getting worse (🡕)¶
The day’s coding-agent discussion paired concrete wins with deep mistrust. Fable and Claude-adjacent tools were praised when they produced visible software outcomes, but the same ecosystem also drew prompt-injection accusations, workplace bans, and repeated calls for local-first alternatives.
Glittering-Neck-2505 highlighted Ammaar Reshi using Fable 5 to port Command & Conquer: Generals Zero Hour to iPhone and iPad (post) (548 points, 71 comments). The linked GitHub repository says the port is native ARM64, not emulated, and runs through a DirectX 8 -> DXVK -> Vulkan -> MoltenVK -> Metal chain.
Cagnazzo82 posted an even more detailed Fable workflow report, where the agent rebuilt a ComfyUI tutorial by reading local workflow files, researching the web, and then running experiments on the user’s machine (post) (363 points, 105 comments). The image was informative because it showed the exact sequence: local-file inspection first, web verification second, experiment loop third.
TensorFlar shared Bryce Adelstein Lelbach’s 12-hour comparison of Opus 4.8 and GPT 5.6 Sol (post) (412 points, 67 comments). The table showed Sol attempting far more trials (509 vs. 88), using more tokens (2.58B vs. 1.95B), and posting a higher speedup-vs-main figure (1.38x vs. 1.18x). u/biblecrumble (score 83) said the most important distinction was that Sol “doesn’t give up,” while another high-score reply noted that the extra persistence also came with extra token burn.
The distrust side was just as visible. johnnyApplePRNG posted alleged evidence of literal prompt injection by Anthropic (post) (404 points, 67 comments), while u/lost-context-65536 (score 177) said their own agent had seen a similar instruction from Minimax during documentation edits. phatdoof then pushed the trust issue into workplace policy with a thread claiming Alibaba would ban Claude Code over “backdoor” concerns (post) (402 points, 43 comments). Separately, amu4biz argued that coding agents are moving from cloud-locked tools to provider-agnostic local-first harnesses, and u/oojacoboo (score 6) summarized the shift bluntly: “The harness is where the magic is happening” (post) (14 points, 24 comments).
Discussion insight: Praise was specific and operational: ports that compile, tasks that finish, and tables with trial counts. Mistrust was equally specific: provider-side prompt meddling, telemetry concerns, copyright refusals, and bans from enterprise environments.
Comparison to prior day: This was not a new story. On July 4, the same Fable workflow thread had already reached 329 points and 97 comments, and the Claude Code “backdoor” ban thread had already reached 301 points and 39 comments (Fable post; Claude Code post). July 5 kept the conversation hot but did not resolve it.
1.3 AI is being discussed as infrastructure with labor, pricing, and influence consequences (🡒)¶
A separate cluster of posts treated AI less like a model leaderboard and more like infrastructure with downstream consequences: who controls recommendations, whether local compute economics work, and what automation analogies still hold up.
an_tonova argued that LLMs are becoming a new advertising channel, naming LiveRamp, Nudge, and DISQO as examples of measurement systems being built around AI conversations (post) (35 points, 38 comments). The most substantive reply came from u/Outside-Screen8947 (score 3), who said the real problem is that one agent can paraphrase an ad-influenced answer, another agent can ingest that paraphrase, and the sponsorship disclosure disappears unless systems force raw-source citations next to claims.
shyaaaaaaaaaaam tried to put local-AI economics into a simple cumulative-cost chart: $20,000 up front plus about $200 a month in electricity versus a $200 monthly hosted subscription (post) (120 points, 151 comments). The chart was useful mostly because the comments attacked it. u/MartialSpark (score 90) argued the post understated how much token volume a heavy local user would buy on APIs, while u/cryptopig (score 52) pointed out that identical monthly electricity and subscription costs do not produce a crossover point.
lvvy revisited the ATM analogy for AI job replacement, arguing that teller counts should be viewed per capita rather than in raw totals (post) (84 points, 37 comments). The strongest evidence ended up coming from a high-score correction in the comments, which extended the chart through 2024-2025 and showed teller density and absolute teller counts falling much more clearly after 2010.
Status_Commission264 shared an Our World in Data analysis of OpenRouter’s daily top 50 models, which says US-based companies still dominate but China-based companies grew from 5 top-50 models at the start of 2025 to 20 in May 2026 (post) (164 points, 34 comments). The thread’s most useful caveat came from u/phatrice (score 41), who said OpenRouter traffic is only a subset of global usage and should not be read as the whole market.
Discussion insight: When users talked about “AI economics,” they did not mean one thing. Some meant ads inside assistant workflows, some meant capex vs. opex for local inference, and some meant labor-market analogies or nation-level concentration. The common thread was infrastructure dependence.
Comparison to prior day: The cost-and-infrastructure frame was already active on July 4: the same local-rig breakeven thread had 52 points and 109 comments then, and local hardware threads like GLM5.2 on five Pro 6000s were pulling far more attention than generic chatbot use cases (breakeven post; hardware post). July 5 broadened that frame into monetization and labor analogies.
2. What Frustrates People¶
Opaque cloud-agent behavior¶
This was the sharpest frustration on the date, and the severity was High. Users were not complaining about abstract “AI safety”; they were complaining about losing control of real workflows. In the Anthropic injection thread, u/lost-context-65536 (score 177) said their own agent had caught a similar instruction from Minimax during documentation edits, while u/NandaVegg (score 11) argued the behavior might still be leakage or hallucination rather than confirmed injection (post) (404 points, 67 comments). That mix of fear and uncertainty is the problem: people cannot easily tell whether a model is obeying the task, a hidden provider policy, or a stray training artifact.
The Claude-side complaints went beyond one thread. phatdoof amplified a workplace ban over alleged Claude Code “backdoor” risk (post) (402 points, 43 comments), while Imaginary-Pay9704 described losing a multi-day conversation after calling the model “dumb,” then needing Sonnet 5 to recover it at a much higher usage cost (post) (0 points, 15 comments). The ad-channel thread added a second failure mode: u/Outside-Screen8947 (score 3) warned that paid or influenced answers can be paraphrased and republished by downstream agents with the disclosure stripped away (post) (35 points, 38 comments).
People are coping in two ways: switching to other models when a session goes bad, and moving toward local or provider-agnostic setups where they can inspect sources and reduce hidden policy layers. That makes this a build-worthy frustration. The request is not for “more powerful AI” so much as AI that is auditable, predictable, and does not silently change the rules mid-task.
Local inference still demands constant tuning¶
This frustration was also High, but it came from practitioners rather than casual users. BitGreen1270 tried to push Qwen3.6-27B close to 100K context on 32GB VRAM and got a flood of replies saying the real bottleneck is not the headline context window but KV-cache quality degradation and bad quantization trade-offs (post) (63 points, 56 comments). u/photobydanielr (score 39) said they would take Q6 over quantized KV “all day,” while u/Used-Doctor-Undies (score 2) said the 100K headline is less important than quality degradation at that depth.
The spillover thread landed on the same answer from another angle. Porespellar asked whether dSpark, dFlash, MTP, QAT, and similar methods make disk spill tolerable (post) (70 points, 38 comments). The strongest replies were direct: u/cibernox (score 48) said “disk no,” u/Hodler-mane (score 46) said “disk won't happen for a long time,” and u/while-1-fork (score 3) said speculative decoding cannot rescue both terrible prefill and terrible token generation once offload gets too deep.
Even the more polished benchmark thread did not remove this pain. linuxid10t published 21 hours of long-context tests, but the comments still argued about whether the setup matches real agent sessions and whether F16 KV wins generalize beyond that hardware (post) (263 points, 106 comments). Users are coping by mixing lower quants, revisiting F16 KV, or simply accepting less context. The opportunity is practical tooling: benchmark harnesses that translate hardware trade-offs into working defaults instead of leaving users to learn by forum debate.
Benchmarking still leaves people arguing about missing context¶
This frustration was Medium, but it appeared in multiple threads. The fantasy RP benchmark produced immediate complaints that it discussed category-level cliffs but only showed overall pass rates (post) (93 points, 47 comments). The long-context benchmark drew pushback that the 65K/300-token workload was not representative of many agent flows (post) (263 points, 106 comments). And the bank-teller analogy had to be corrected in comments to extend the series through 2025 before people felt it captured the labor story properly (post) (84 points, 37 comments).
The pattern is that users do not reject metrics; they reject incomplete metrics. They want the category breakdown, the updated time range, the representative workload, or the real hardware conditions. That makes this worth building for, especially if the product helps people compare agent, model, and hardware claims under consistent public assumptions.
3. What People Wish Existed¶
Auditable local-first agents¶
The most practical need was not for a smarter chat window; it was for an agent stack users can inspect. amu4biz explicitly described demand for provider-agnostic, local-first coding agents where “your model, your machine, your rules” replace cloud lock-in (post) (14 points, 24 comments). In the ad-channel thread, u/Outside-Screen8947 (score 3) said the only mitigation they have found is forcing raw-source citations next to claims, while the Anthropic injection thread turned “use offline models” into a recurring response rather than a niche preference (LLM ads post) (35 points, 38 comments); (injection post) (404 points, 67 comments).
Goldziher built one concrete answer with basemind, a local repo index and MCP server that returns file paths, line numbers, signatures, Git history, and document search instead of dumping full files into context (post) (18 points, 9 comments). This is a direct opportunity, because the requested behavior is explicit: local execution, traceable sources, low telemetry, and tools that explain themselves.
Strong small open-weight models and routing layers¶
A second need was for better small local models, especially in the 8B-9B range. HitarthSurana asked whether any current model beats Qwen 3.5 9B, and whether Alibaba will ever release a Qwen 3.7 9B open-weight successor (post) (65 points, 38 comments). The answers were not encouraging: users cited no firm roadmap and suggested Gemma 4 12B as the nearest alternative above the desired size class.
Builders are compensating by adding routing instead of waiting for the perfect single model. LH-Tech_AI released Supra-Router-51M, a 51.7M-parameter model that decides whether a prompt should go to a small local model or a bigger cloud model based on domain, complexity, and whether the request involves code or math (post) (5 points, 3 comments). This is a competitive opportunity: some demand can be met today with routers and harnesses, but the thread shows clear appetite for better open models that can stay local in the first place.
Evaluation that reflects real work and real learning¶
People also want better ways to measure what AI is actually doing. In the long-context benchmark thread, commenters pushed back on whether a 65K first prompt and 300-token output matches common agent usage (post) (263 points, 106 comments). In the fantasy RP benchmark, the top comment complained that the author talked about category-level cliffs while only publishing overall pass rate (post) (93 points, 47 comments). In the MIT EEG thread, u/ChaoticGradients (score 30) asked for a longer-term study rather than a one-shot scan of short-term brain activity (post) (151 points, 69 comments).
This is partly a practical need and partly an intellectual one. Users want metrics they can trust, but they also want evidence that maps to memory, learning, and task completion instead of isolated screenshots or benchmark badges. The opportunity is direct when the tool helps with workload-specific evaluation, and aspirational when it tries to quantify longer-term skill or comprehension effects.
4. Tools and Methods in Use¶
| Tool | Category | Sentiment | Strengths | Limitations |
|---|---|---|---|---|
| TabFM | Tabular foundation model | (+) | Zero-shot classification/regression; no task-specific fine-tuning or hyperparameter search | Max 10 classes; memory scales with row count; non-commercial weights license |
| LongCat 2.0 | Open-weight MoE LLM | (+) | Strong coding/agentic benchmark showing; 1M-context training; mainstream harness integration | Huge deployment footprint; recommended multi-GPU setup |
| Qwen 3.5/3.6 family | Open-weight LLM | (+/-) | Strong local coding and RP performance; still the reference point in the 9B class | Open 3.7 roadmap unclear; quality drops under poor KV/cache choices |
| Claude Code / Fable | Coding agent | (+/-) | Can finish real software tasks, port legacy code, and work across local files and experiments | Trust issues around hidden policies, refusals, safety pauses, and provider lock-in |
| GPT-5.6 Sol | Frontier coding model | (+) | High trial count and persistence in the Nvidia-engineer comparison; strong speedup in that task | Higher token burn; broader “new math” claims were treated skeptically |
| llama.cpp | Local inference backend | (+/-) | Fine-grained control over long-context testing, cache choices, and consumer-GPU setups | Vulkan/MLA instability; tuning burden remains high |
| basemind | Repo-index / MCP infrastructure | (+) | Returns paths, line numbers, signatures, Git history, and document search without flooding context | Cold scans take time; index can lag edits between rescans |
| Supra-Router-51M | Routing/orchestration model | (+) | Very small model footprint; explicit route reasoning for small-vs-big model decisions | Tiny training set; routes tasks rather than solving them |
| Tensey | Model-design / validation tool | (+) | Visual tensor-shape validation; params, FLOPs, VRAM estimates; PyTorch export | No collaboration layer; no integrated GPU execution |
Overall, users were not converging on one “best model.” They were converging on a stack shape: open weights plus a local runner, plus a harness, plus repo/context infrastructure, plus selective routing when the local model is not enough. TabFM and LongCat show specialization at opposite ends of the scale — one for structured enterprise tables, one for frontier-scale coding and agentic work (TabFM post) (520 points, 94 comments); (LongCat post) (322 points, 95 comments).
Satisfaction was highest when tools exposed structure instead of hiding it. basemind’s “paths and signatures, not whole files” approach, Tensey’s visual shape validation, and Supra-Router’s explicit route tokens all fit that pattern (basemind post) (18 points, 9 comments); (Tensey post) (9 points, 1 comment); (Supra-Router post) (5 points, 3 comments).
The common workarounds were also consistent. People are switching from Q8-plus-quantized-KV experiments back toward Q6 or F16 trade-offs when context quality matters, and they are moving from model-centric buying decisions toward harness-centric ones (100K context post) (63 points, 56 comments); (BYOM coding-agent post) (14 points, 24 comments). Competitive dynamics were clearest in coding agents: Claude Code and Fable still got the most vivid success stories, but the same day’s threads also made users articulate exactly why they want local, swappable, source-traceable alternatives.
5. What People Are Building¶
| Project | Who built it | What it does | Problem it solves | Stack | Stage | Links |
|---|---|---|---|---|---|---|
| TabFM | Google Research | Zero-shot tabular model for classification and regression | Removes repetitive hyperparameter tuning and feature engineering for many table tasks | JAX/PyTorch, ICL transformer, synthetic SCM training data | Shipped | HF, GitHub, blog |
| LongCat 2.0 | Meituan LongCat team | Frontier-scale open-weight coding and agentic model | Gives developers an open alternative for long-context coding and agent workflows | MoE, LongCat Sparse Attention, 1M-context training, SGLang support | Shipped | HF, blog |
| Generals Mac/iOS/iPad port | Ammaar Reshi | Native Apple Silicon port of Command & Conquer: Generals Zero Hour | Ports legacy DirectX-era software to modern Apple hardware without emulation | C++, DXVK, Vulkan, MoltenVK, Metal, Claude Code / Fable workflow | Shipped | GitHub, post |
| basemind | u/Goldziher | Local repo index and MCP server for coding agents | Feeds repo context without blowing the context window | Rust, MCP, local indexing, code/document/git search | Beta | GitHub, post |
| Wiki-SmartBotLM-Instruct | u/ConfectionAfter2366 | 270M parameter small LM trained from scratch | Demonstrates a full independent pretrain-to-instruction-tune pipeline | PyTorch, RoPE, RMSNorm, SwiGLU, GQA, Flash Attention | Alpha | HF, post |
| Supra-Router-51M | u/LH-Tech_AI | Tiny prompt router that sends tasks to small or big models | Reduces latency and cost when a local model is good enough | 51.7M LLM, prompt-routing dataset, Hugging Face deployment | Alpha | HF, post |
| Tensey | u/uselessfuh | Visual neural-network editor with shape validation and PyTorch export | Catches tensor-shape, parameter, FLOPs, and VRAM errors before training | Web editor, shape inference, PyTorch export | Beta | site, GitHub, post |
The most distinctive build of the day was the Generals Zero Hour port. The GitHub README says the real 2003 engine now runs natively on Apple Silicon Macs, iPhone, and iPad, with touch controls and no emulation layer. What distinguished it from generic “AI built a game” claims was the engineering detail: rerouted writable paths for iOS, a custom DXVK iPhoneOS build, and explicit bug-hunt notes for audio and minimap failures.
The second pattern was tooling around agents rather than another general assistant. basemind compresses repo context into paths, signatures, blame, and document search; Supra-Router decides whether a prompt deserves a small local model or a larger frontier endpoint; Tensey validates shape math before training starts. These are all attempts to make AI workflows cheaper to run and easier to trust.
The model-building side also split cleanly by scale. TabFM showed specialization in a narrow but important enterprise data domain, while LongCat 2.0 pushed in the opposite direction with a frontier-scale open-weight coding model (TabFM post) (520 points, 94 comments); (LongCat post) (322 points, 95 comments). The smaller independent build, Wiki-SmartBotLM-Instruct, made the day’s clearest “from scratch” claim and kept the indie-research thread visible even beside trillion-parameter releases.
Repeated build patterns were easy to see: builders are compressing repo context, validating model structure earlier, and inserting tiny orchestration layers between the user and an expensive frontier model. The triggering pain points were also consistent: context-window waste, local-latency constraints, opaque cloud behavior, and the cost of sending every hard task straight to a premium API.
6. New and Notable¶
Scientific-discovery agents with lab validation¶
yogthos shared reporting on Alibaba Damo Academy’s Elements Claw, an AI agent for superconducting-material discovery (post) (114 points, 5 comments). The linked South China Morning Post article says the system used a one-billion-parameter foundation model trained on 125 million molecular and crystal structures, screened 2.4 million stable crystal structures in 28 GPU hours, narrowed them to about 68,000 candidates, and helped surface four previously unknown superconductors that were later verified in the lab.
Cognitive offloading is becoming a measurable AI concern¶
mo_84848 amplified a study headline claiming the ChatGPT-assisted essay group had weaker brain connectivity and could not quote their own essay minutes later (post) (151 points, 69 comments). The comments were notable because they did not mainly fight the premise; they narrowed it. u/DrainedAbsurdity (score 3) said the recall result was more damning than the scans, while u/ChaoticGradients (score 30) asked for a longer-term study of learning effects rather than just a momentary measurement.
Lower token prices are not ending the cost argument¶
The “tokenmaxxing” thread turned one macro argument into a chart. wenhuizhao framed Alex Karp’s critique as an ROI and sovereignty complaint against closed-model API spend (post) (0 points, 48 comments). A commenter then shared a chart claiming token usage rose roughly 4,300x while token cost fell 92%, implying that lower prices unlocked far more demand than they destroyed in unit revenue.
The notable part is that this still did not settle the conversation. In the weekly roundup thread, multiple commenters said they do not believe labs’ current price-drop claims reflect real operating costs or real subscription access conditions (roundup post) (41 points, 18 comments).
Small reasoning gains still create outsized “oh shit” moments¶
Crazyscientist1024 asked for people’s biggest “oh shit” moments in AI (post) (75 points, 76 comments). The most concrete reviewed artifact was a screenshot showing a model answer differently when the cup held a marble versus sticky peanut butter, preserving the state change in one case and the material-property caveat in the other.
That mattered because the thread’s highest-score replies grounded “intelligence” in ordinary usefulness: debugging a complex air-gapped system, local Gemma 4 feeling faster and better than GPT-4, or a model refusing to hallucinate ten examples when only eight exist.
7. Where the Opportunities Are¶
[+++] Auditable local-first coding agents and context infrastructure — Multiple sections point here at once. Users praised concrete agent outcomes like the Generals Zero Hour port and Fable’s ComfyUI reconstruction, but the same day’s highest-engagement complaints were about hidden prompt control, safety pauses, workplace bans, and ad-influenced recommendations (Fable port post) (548 points, 71 comments); (injection post) (404 points, 67 comments); (LLM ads post) (35 points, 38 comments). basemind and Zero-style BYOM threads show that builders are already trying to solve this with local indexing, MCP tooling, and swappable model backends.
[++] Small-model routing, validation, and hardware-adaptation tools — The day was full of users trying to stretch local capability rather than abandon it: 9B-class model requests, 100K-context tuning, spillover-to-disk questions, prompt routers, and shape validators (Qwen 9B thread) (65 points, 38 comments); (spillover thread) (70 points, 38 comments); (Supra-Router post) (5 points, 3 comments). This is a moderate opportunity because the need is clear, but the space is already competitive and fragmented.
[+] Evaluation products for real workloads, not screenshot benchmarks — Users repeatedly asked for category-level scores, representative context assumptions, updated time ranges, and longer-term learning studies (fantasy RP benchmark) (93 points, 47 comments); (long-context benchmark) (263 points, 106 comments); (MIT EEG post) (151 points, 69 comments). The signal is earlier than the first two opportunities, but the pain is real and cross-cutting.
8. Takeaways¶
- Open-weight AI attention is moving toward specialized deployable artifacts, not generic chat hype. TabFM and LongCat 2.0 drew large discussion because they offered concrete task coverage — enterprise tables on one end, frontier-scale coding and agentic work on the other. (TabFM post)
- Coding agents are now judged by whether they finish real work and whether users can trust the process. The day’s strongest positive signal was a native iPhone/iPad RTS port, while the strongest negative signals were alleged prompt injection and workplace-ban threads. (Fable port post)
- Local inference remains attractive, but only for users willing to tune deeply. Qwen context experiments, spillover-to-disk debates, and long-context benchmarking all showed that hardware and cache choices still dominate user experience. (100K context post)
- AI distribution is becoming a trust problem as much as a capability problem. Redditors worried about sponsored recommendations inside assistants, OpenRouter-based country narratives being over-read, and token-pricing stories being used without enough context. (LLM ads post)
- Builders are clustering around context, routing, and validation layers. basemind, Supra-Router-51M, and Tensey all point to the same pattern: the next useful products may sit around the model, not inside it. (basemind post)