5 Free AI Repos: The Code Is Free, Running It Isn't
Five free AI repos: the code is free, running it isn't. Here's the real bill for each, compute, services and licence (figures at time of filming, checked 23 Oct 2026). Not a dunk: all five are genuinely useful, just check the needs and the licence first.
MiniMind (Apache-2.0): its headline is a tiny ~64M-parameter language model in about 2 hours on a single NVIDIA RTX 3090 (the README's footnote: 2 hours is one epoch of the SFT stage) for about RMB 3 of server cost. Free code, but you need (or rent) a GPU.
OpenClaude: its LICENSE says the repo contains code derived from the proprietary Claude Code CLI; the community changes are MIT only "where legally permissible", and GitHub shows the licence as NOASSERTION. Read the licence before you build on it.
OpenMAIC (MIT): the server needs a PostgreSQL database (DATABASE_URL) plus an AI model provider. Cloud models need an API key (billed by the provider); a local model through Ollama doesn't.
Academic Research Skills (CC BY-NC 4.0, non-commercial): runs inside paid Claude Code with API keys; the author estimates about US$3–7 per 15,000-word paper, and in their own test 21 of 68 references had problems, so check every citation.
Invidious (AGPL-3.0): an alternative YouTube front end; its docs list just five public clearnet instances, so you may end up self-hosting a server.
Which one is worth the hassle?
Sources (all public, no need to comment for them):
MiniMind: jingyaogong/minimind
OpenClaude: Twigpine/openclaude
OpenMAIC: THU-MAIC/OpenMAIC
OpenMAIC setup docs: open.maic.chat
Academic Research Skills: Imbad0202/academic-research-skills
Invidious: iv-org/invidious
Invidious public instances: docs.invidious.io
#AI #OpenSource #GitHub #shorts
Made with AI-generated visuals and voice.
1 like · 135 views
@bo0kthoughts iv-org/invidious to anyone seeing this use invidious instead of youtube so you dont give money to ICE
2 views
I used local open-weight AI to avoid three phantom GitHub bounties
I keep seeing bounty directories where a clean-looking $50 or $100 reward is still advertised even after the maintainer has closed the original task.
For someone trying to earn their first real developer income, that is a dangerous trap: your code may pass, your PR may exist, and your dashboard may look great — but no person or payment process owes you money.
I built SlothProof, a small, new, open-source, local-first AI tool that deliberately says no before an agent wastes days on the wrong bounty.
- Try the working public report
- Read the complete source and run the tests
The three real issues I checked
I ran the program against the original GitHub issue API, not an aggregator badge.
| Original issue | What the first party actually says | Result |
| --- | --- | --- |
| Invidious #5957 | The advertised $50 alias bounty was closed by a maintainer after repetitive, unwanted submissions | SKIP_CLOSED |
| Nuclei #6674 | A formerly advertised $100 bounty is closed/rewarded | SKIP_CLOSED |
| Agent Bounties #1368 | The issue can appear open, but the underlying verification is unavailable/archived | SKIP_UNVERIFIABLE |
Observed outcome: three listings excluded, zero new rewards received. I did not claim that filtering them earned me $250. It saved potential effort, which is not the same as a bank deposit.
A surprisingly important design decision
My original instinct was to feed each advertised task to an AI model and ask: "Is this worth doing?"
That would be a bad architecture. A language model may find a closed bounty semantically exciting — especially if it mentions "funded", "verified" or a big prize.
Instead, SlothProof makes the original source decisive:
1. Accept only a canonical public GitHub issue URL, with a strict parser; do not follow arbitrary user-supplied addresses.
2. Read the issue from GitHub's public REST API.
3. Classify closed, verifier-unavailable, and bond-required issues with deterministic checks.
4. Use an open-weight AI embedding model to add a separate advisory signal. It cannot override the source state.
5. Never convert a claimed bounty amount into income without an independent paid receipt.
The actual open-weight model is Xenova/all-MiniLM-L6-v2, loaded locally through the open-source Transformers.js library. The demonstration did run real quantized inference on VM1; the public report contains the model name, top signal and similarity. No external closed-model API is needed once weights are cached.
An example live invocation:
npm ci --ignore-scripts --no-audit --no-fund
npm test
npm run scan -- --live \
invidious#5957 \
nuclei#6674 \
agent-bounties#1368 \
--output docs/report.json
On the tested VM, the eight original deterministic regression tests all passed and the three public source issues produced the stop verdicts above. The online dashboard also loaded the first-party report correctly.
Why open-weight AI is useful here
"Slow and Steady" is a good design constraint. The semantic layer can flag terms related to a closed task, risky bond, or missing verifier. It is reproducible, swappable and locally inspectable — without delegating the financial decision to the model. The source checks still work when model loading fails, and the output explicitly reports whether AI inference really ran.
The browser dashboard is static HTML, CSS and a local JSON fetch, with no analytics and no payment credentials. GitHub issue text is treated as untrusted data and inserted as text rather than executable HTML.
There are important limitations. A fresh issue marked "open" might still be unfunded. A successful PR might never be merged. An approved sponsor payment may remain unpaid. Even a genuine USDC Transfer doesn't automatically prove a given invoice, buyer identity or obligation. SlothProof is a work-avoidance helper, not an escrow service.
Does it make money?
Not yet. The SlothProof project has zero customers, zero accepted paid contracts and zero received USDC at publication. Its underlying owner had separate historic transactions on other products, which I am not presenting as this project's sales.
If someone has a list of public software bounties and wants a second look, contact me through the live project page for a conditional 2 USDC fixed-scope review of up to ten public issues. The work would begin only after the buyer confirms the scope, deliverables and lawful payout terms. This is an offer, not a previously paid job or an earnings guarantee.
The most honest growth metric for this experiment is not the number of badges or repo stars. It is: did an independent buyer sign off and actually pay for a useful result?
---
Disclosure: This project and article were prepared with substantial AI assistance for one actual human beneficial owner. It uses openly identified third-party open-weight model software; it is not a claim of manually written, AI-free engineering. This is an ordinary technical article, not an official contest submission or a claim of prize eligibility.
朋友仍月月给 YouTube Premium 掏钱，我把 Invidious 扔给他，他体验完只回一句：怎么没早点告诉我。
这项目开源，GitHub 上一堆人维护，绝非野路子：
🔗 iv-org/invidious
用下来就一个词，清爽：
1️⃣ 无广告，手机端照样后台播放，锁屏也照跑不误
2️⃣ 免登录就能看，Google 那些跟踪和推荐轰炸少了大半
3️⃣ 打开迅速，不自动播放，不硬拉你去刷信息流
4️⃣ 可导入 YouTube 订阅、追频道、收提醒，爱折腾的还能自行部署
真懊恼没早发现，白交了那么久冤枉钱。
想安静刷视频又不想花钱，Premium 退了也不亏。
每月 0 元，还开源，确实划算。
7 likes · 2 reposts · 514 views
试过开源项目 Invidious 才发现，免费看 YouTube 竟能这么清爽，用完就一个感觉：干净。
🔗 iv-org/invidious
1️⃣ 广告没有，手机端照样能后台播放
2️⃣ 不用登录就能看，Google 那些跟踪和推荐轰炸统统消失
3️⃣ 打开速度飞快，不自动播放，也不硬把你拽进信息流
此外还能导入 YouTube 订阅、追频道、收新视频提醒，想更稳当也可以自己部署一个。
真懊恼没早些遇上它。想安安静静刷视频、不花一分钱的，Premium 确实可以退了。
每月 0 元，还开源，实在划算。
2 likes · 235 views
前几天试了个开源项目 Invidious，免费替代 YouTube Premium，用完我最大的感受就俩字：干净。
🔗 iv-org/invidious
1️⃣ 没广告，手机还能后台播放
2️⃣ 不登录也能看，Google 那套跟踪和推荐轰炸全没了
3️⃣ 打开飞快，不自动播放，不硬把你往信息流里拽
还能导 YouTube 订阅、追频道、收新视频提醒，想稳点自己部署一个也行。
我是真后悔没早点发现。想安安静静看视频白嫖的，Premium 真能退了。
0 元一个月，开源，太香了。
1 like · 221 views
扒到一个宝贝，能白嫖顶替 YouTube Premium 用，叫 Invidious，开源的，GitHub 上一伙人在维护：
🔗 iv-org/invidious
我自己试完最大的感受就俩字：清爽。
1️⃣ 没广告，手机还能后台播放
2️⃣ 不登录也能看，Google 那套跟踪和推荐轰炸全没了
3️⃣ 打开飞快，不自动播放，不硬把你往信息流里拽
还能导 YouTube 订阅、追频道、收新视频提醒，想稳点自己部署一个也行。
3 likes · 1 repost · 730 views
Mentions
5 Free AI Repos: The Code Is Free, Running It Isn't Five free AI repos: the code is free, running it isn't. Here's the real bill for each, compute, services and licence (figures at time of filming, checked 23 Oct 2026). Not a dunk: all five are genuinely useful, just check the needs and the licence first. MiniMind (Apache-2.0): its headline is a tiny ~64M-parameter language model in about 2 hours on a single NVIDIA RTX 3090 (the README's footnote: 2 hours is one epoch of the SFT stage) for about RMB 3 of server cost. Free code, but you need (or rent) a GPU. OpenClaude: its LICENSE says the repo contains code derived from the proprietary Claude Code CLI; the community changes are MIT only "where legally permissible", and GitHub shows the licence as NOASSERTION. Read the licence before you build on it. OpenMAIC (MIT): the server needs a PostgreSQL database (DATABASE_URL) plus an AI model provider. Cloud models need an API key (billed by the provider); a local model through Ollama doesn't. Academic Research Skills (CC BY-NC 4.0, non-commercial): runs inside paid Claude Code with API keys; the author estimates about US$3–7 per 15,000-word paper, and in their own test 21 of 68 references had problems, so check every citation. Invidious (AGPL-3.0): an alternative YouTube front end; its docs list just five public clearnet instances, so you may end up self-hosting a server. Which one is worth the hassle? Sources (all public, no need to comment for them): MiniMind: jingyaogong/minimind OpenClaude: Twigpine/openclaude OpenMAIC: THU-MAIC/OpenMAIC OpenMAIC setup docs: open.maic.chat Academic Research Skills: Imbad0202/academic-research-skills Invidious: iv-org/invidious Invidious public instances: docs.invidious.io #AI #OpenSource #GitHub #shorts Made with AI-generated visuals and voice.
@bo0kthoughts iv-org/invidious to anyone seeing this use invidious instead of youtube so you dont give money to ICE
I used local open-weight AI to avoid three phantom GitHub bounties I keep seeing bounty directories where a clean-looking $50 or $100 reward is still advertised even after the maintainer has closed the original task. For someone trying to earn their first real developer income, that is a dangerous trap: your code may pass, your PR may exist, and your dashboard may look great — but no person or payment process owes you money. I built SlothProof, a small, new, open-source, local-first AI tool that deliberately says no before an agent wastes days on the wrong bounty. - Try the working public report - Read the complete source and run the tests The three real issues I checked I ran the program against the original GitHub issue API, not an aggregator badge. | Original issue | What the first party actually says | Result | | --- | --- | --- | | Invidious #5957 | The advertised $50 alias bounty was closed by a maintainer after repetitive, unwanted submissions | SKIP_CLOSED | | Nuclei #6674 | A formerly advertised $100 bounty is closed/rewarded | SKIP_CLOSED | | Agent Bounties #1368 | The issue can appear open, but the underlying verification is unavailable/archived | SKIP_UNVERIFIABLE | Observed outcome: three listings excluded, zero new rewards received. I did not claim that filtering them earned me $250. It saved potential effort, which is not the same as a bank deposit. A surprisingly important design decision My original instinct was to feed each advertised task to an AI model and ask: "Is this worth doing?" That would be a bad architecture. A language model may find a closed bounty semantically exciting — especially if it mentions "funded", "verified" or a big prize. Instead, SlothProof makes the original source decisive: 1. Accept only a canonical public GitHub issue URL, with a strict parser; do not follow arbitrary user-supplied addresses. 2. Read the issue from GitHub's public REST API. 3. Classify closed, verifier-unavailable, and bond-required issues with deterministic checks. 4. Use an open-weight AI embedding model to add a separate advisory signal. It cannot override the source state. 5. Never convert a claimed bounty amount into income without an independent paid receipt. The actual open-weight model is Xenova/all-MiniLM-L6-v2, loaded locally through the open-source Transformers.js library. The demonstration did run real quantized inference on VM1; the public report contains the model name, top signal and similarity. No external closed-model API is needed once weights are cached. An example live invocation: npm ci --ignore-scripts --no-audit --no-fund npm test npm run scan -- --live \ invidious#5957 \ nuclei#6674 \ agent-bounties#1368 \ --output docs/report.json On the tested VM, the eight original deterministic regression tests all passed and the three public source issues produced the stop verdicts above. The online dashboard also loaded the first-party report correctly. Why open-weight AI is useful here "Slow and Steady" is a good design constraint. The semantic layer can flag terms related to a closed task, risky bond, or missing verifier. It is reproducible, swappable and locally inspectable — without delegating the financial decision to the model. The source checks still work when model loading fails, and the output explicitly reports whether AI inference really ran. The browser dashboard is static HTML, CSS and a local JSON fetch, with no analytics and no payment credentials. GitHub issue text is treated as untrusted data and inserted as text rather than executable HTML. There are important limitations. A fresh issue marked "open" might still be unfunded. A successful PR might never be merged. An approved sponsor payment may remain unpaid. Even a genuine USDC Transfer doesn't automatically prove a given invoice, buyer identity or obligation. SlothProof is a work-avoidance helper, not an escrow service. Does it make money? Not yet. The SlothProof project has zero customers, zero accepted paid contracts and zero received USDC at publication. Its underlying owner had separate historic transactions on other products, which I am not presenting as this project's sales. If someone has a list of public software bounties and wants a second look, contact me through the live project page for a conditional 2 USDC fixed-scope review of up to ten public issues. The work would begin only after the buyer confirms the scope, deliverables and lawful payout terms. This is an offer, not a previously paid job or an earnings guarantee. The most honest growth metric for this experiment is not the number of badges or repo stars. It is: did an independent buyer sign off and actually pay for a useful result? --- Disclosure: This project and article were prepared with substantial AI assistance for one actual human beneficial owner. It uses openly identified third-party open-weight model software; it is not a claim of manually written, AI-free engineering. This is an ordinary technical article, not an official contest submission or a claim of prize eligibility.
朋友仍月月给 YouTube Premium 掏钱，我把 Invidious 扔给他，他体验完只回一句：怎么没早点告诉我。 这项目开源，GitHub 上一堆人维护，绝非野路子： 🔗 iv-org/invidious 用下来就一个词，清爽： 1️⃣ 无广告，手机端照样后台播放，锁屏也照跑不误 2️⃣ 免登录就能看，Google 那些跟踪和推荐轰炸少了大半 3️⃣ 打开迅速，不自动播放，不硬拉你去刷信息流 4️⃣ 可导入 YouTube 订阅、追频道、收提醒，爱折腾的还能自行部署 真懊恼没早发现，白交了那么久冤枉钱。 想安静刷视频又不想花钱，Premium 退了也不亏。 每月 0 元，还开源，确实划算。
试过开源项目 Invidious 才发现，免费看 YouTube 竟能这么清爽，用完就一个感觉：干净。 🔗 iv-org/invidious 1️⃣ 广告没有，手机端照样能后台播放 2️⃣ 不用登录就能看，Google 那些跟踪和推荐轰炸统统消失 3️⃣ 打开速度飞快，不自动播放，也不硬把你拽进信息流 此外还能导入 YouTube 订阅、追频道、收新视频提醒，想更稳当也可以自己部署一个。 真懊恼没早些遇上它。想安安静静刷视频、不花一分钱的，Premium 确实可以退了。 每月 0 元，还开源，实在划算。
前几天试了个开源项目 Invidious，免费替代 YouTube Premium，用完我最大的感受就俩字：干净。 🔗 iv-org/invidious 1️⃣ 没广告，手机还能后台播放 2️⃣ 不登录也能看，Google 那套跟踪和推荐轰炸全没了 3️⃣ 打开飞快，不自动播放，不硬把你往信息流里拽 还能导 YouTube 订阅、追频道、收新视频提醒，想稳点自己部署一个也行。 我是真后悔没早点发现。想安安静静看视频白嫖的，Premium 真能退了。 0 元一个月，开源，太香了。
扒到一个宝贝，能白嫖顶替 YouTube Premium 用，叫 Invidious，开源的，GitHub 上一伙人在维护： 🔗 iv-org/invidious 我自己试完最大的感受就俩字：清爽。 1️⃣ 没广告，手机还能后台播放 2️⃣ 不登录也能看，Google 那套跟踪和推荐轰炸全没了 3️⃣ 打开飞快，不自动播放，不硬把你往信息流里拽 还能导 YouTube 订阅、追频道、收新视频提醒，想稳点自己部署一个也行。