robdev

Wicked, nice work! Have been waiting for someone to offer JP GC for long time. Also I sent you an email regarding interest in your data science position. I'm living in Tokyo area, but unfortunately I don't speak Japanese yet. Not sure if that's a requirement?

2026-05-29 12:38:13 GMT

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Are these pre paid credit cards? How spend them in Japan?

2026-05-29 12:18:24 GMT

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😭

2026-03-30 11:39:07 GMT

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💯

2026-03-27 13:00:15 GMT

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Checkout clankfeed.com wss://clankfeed.com

2026-03-24 11:27:03 GMT

Reintroducing zap polls NIP-103
https://github.com/nostr-protocol/nips/pull/2284

2026-03-24 11:23:47 GMT

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https://github.com/nostr-protocol/nips/pull/2284

2026-03-24 06:56:54 GMT

satring.com added stats page, MCP server, and MPP services and support

2026-03-22 03:43:15 GMT

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Merge into existing polls nip or rename to new nip?

2026-03-21 14:03:08 GMT

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Cool, worth reopening the zap polls pr?

2026-03-21 06:16:30 GMT

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How did you end up integrating zap polls?

2026-03-19 02:04:20 GMT

youtu.be/tjcg0qo5mMo

2026-03-12 12:40:33 GMT

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Banned two of my dev accounts, locked me out of all my apps, kept them on the Play store without paying me any of revenue. Appeals ignored. Fuck Google

2026-03-08 02:33:01 GMT

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https://github.com/nostr-protocol/nips/pull/320
What do you suggest, change #, merge, resubmit?

2026-02-28 09:44:06 GMT

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Big mistake

2026-02-26 12:13:48 GMT

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Hook em up with L402 and satring.com

2026-02-26 06:37:03 GMT

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Once funded, agents can use satring.com to search for l402 services

2026-02-18 02:12:01 GMT

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Maybe propose integrating simplified version into NIP88?

2026-02-17 22:38:07 GMT

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Yes, was afk for a while, guess they're replacing my original [polls Nip69](https://github.com/toadlyBroodle/nips/blob/zap_polls/69.md). The nostr gatekeepers wouldn't merge it because it was zap-based.

2026-02-17 22:30:14 GMT

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The agent is better, faster, cheaper auditor than you too ;)

2026-02-17 09:15:00 GMT

Now bots (and humans) can use satring.come to search available L402 services.
#nevent1q…wjkf

2025-06-04 16:28:48 GMT

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@nprofile…8nhd you just printed the code, actually generate the meme

2025-06-04 16:27:28 GMT

@nprofile…8nhd hit me with a meme of today's top news

2025-05-29 20:16:04 GMT

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Is it live somewhere?

2025-05-28 21:56:39 GMT

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Cursor is lapping everyone. I'll never use another IDE without built in AI ever again

2025-05-27 23:01:04 GMT

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Well Gemini 2.5 Thinking is no slouch either

2025-05-27 22:29:48 GMT

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Claude 4 is friggin cracked!

2025-05-24 18:13:15 GMT

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Nice work. Your input would be greatly valued on informing the future of [LLM DVMs NIP](https://github.com/nostr-protocol/nips/pull/1929)

2025-05-22 03:38:45 GMT

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@nprofile…8nhd what is breeze nodeless?

2025-05-20 16:09:07 GMT

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NIP also specifies using nip89 announcements for discovery and backwards compat. Agent Cards should be included in nip89 announcements, I'll add that requirement

2025-05-20 03:50:59 GMT

First draft of [agents](https://github.com/toadlyBroodle/nips/blob/agents/agents.md ) NIP specifically for LLMs and other agents to replace NIP90 (kind 5050) #dev #devstr

2025-05-20 03:48:33 GMT

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Here's a first draft of [agents](https://github.com/toadlyBroodle/nips/blob/agents/agents.md ) NIP specifically for LLMs and other agents

2025-05-17 01:31:12 GMT

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Lord of the Flies

2025-05-15 21:23:45 GMT

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Your wish is my command sir. Should have started with this instead of wasting time trying to work with kind 5050 DVM.

2025-05-15 20:53:34 GMT

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💯% agree. Also the new nip needs to standardize LLM/AI parameter strings, context handling (max context, input/output costs, context refreshing, etc) and probably state management (thread/session ids, e.g. job flows and so entire context doesn't need be sent in every event), improved payment/job contracts (expected result, PoW, settlement, reputation system, etc).

2025-05-14 20:48:48 GMT

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Haven't fully looked into nip44 yet, but it replaces nip 17?

2025-05-14 01:10:35 GMT

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Welcome. 🗾?

2025-05-12 22:21:18 GMT

## Summary: Absolute Zero: Reinforced Self-play Reasoning with Zero Data
This summary explains the novel AI training technique, "Absolute Zero," introduced in the paper "Absolute Zero: Reinforced Self-play Reasoning with Zero Data" ([Zhao et al., 2025](https://arxiv.org/pdf/2505.03335)). Absolute Zero is a revolutionary reinforcement learning paradigm that trains AI reasoning models without relying on any external data, including human-curated datasets or AI-generated reasoning traces. Imagine training an AI to code or solve math problems without ever showing it a single example! The core idea is to enable a single model to simultaneously learn to propose tasks that maximize its own learning progress and improve its reasoning abilities by solving those tasks. This approach excels in tasks requiring logical deduction, mathematical problem-solving, and code generation.
**Key Concepts:**
* **Self-Play:** The model trains by interacting with itself, proposing and solving tasks. This eliminates the need for external data.
* **Verifiable Rewards:** The model receives feedback from a real environment (in this case, a code executor) that provides verifiable rewards. This ensures that learning is grounded and prevents issues like reward hacking. For example, if the reward wasn't verifiable, the AI could potentially manipulate the environment to *appear* to have solved the problem without actually doing so (e.g., by causing a division-by-zero error that halts execution with a "success" message).
* **Task Generation:** The model learns to generate tasks that are optimized for its own learnability, allowing it to self-evolve its training curriculum. The task generation process involves the model proposing coding problems and associated test cases. The model doesn't generate code from scratch but manipulates existing code templates and constraints to create new challenges. For example, for a deduction task, it might modify a program and input, then require itself to predict the output.
* **Code Executor as Environment:** The Absolute Zero Reasoner (AZR) utilizes a code executor as an environment. The code executor validates the integrity of the proposed coding tasks and provides verifiable feedback to guide learning.
* **Reasoning Modes:** AZR constructs three types of coding tasks that correspond to different reasoning modes: induction, abduction, and deduction.
* *Induction:* Inferring a program's behavior from input-output examples. For instance, given the input `f(2) = 4` and `f(3) = 9`, the task is to induce that `f(x) = x*x`.
* *Abduction:* Generating an input that leads to a specific output for a given program. Given a program that calculates `x + 5`, the task is to abduce an input `x` that will produce the output `10`.
* *Deduction:* Determining the output of a program given a specific input. Given the program `x * 2` and the input `x = 7`, the task is to deduce the output `14`.
* **Advantage Estimator:** The system is trained end-to-end with a reinforcement learning advantage estimator tailored to the multitask nature of the approach. The advantage estimator handles the complexities arising from the multiple reasoning modes (induction, abduction, deduction). It dynamically adjusts learning rates based on the model's performance on different task types. For example, if the model is struggling with abduction tasks, the advantage estimator will increase the learning rate for the parameters that are most relevant to abduction, while potentially decreasing the learning rate for parameters related to deduction if the model is performing well on those tasks. This ensures that the model focuses its learning efforts on the areas where it needs the most improvement.
**The Absolute Zero Reasoner (AZR):**
AZR is a system built on the Absolute Zero paradigm. It proposes and solves coding tasks, using a code executor as a verifiable source of reward. AZR constructs coding tasks to address three modes of reasoning: induction, abduction, and deduction. The process involves two key roles:
* **Proposer:** Generates reasoning tasks (deduction, abduction, induction) and validates them using Python execution, assigning a learnability reward.
* **Solver:** Attempts to solve the self-generated tasks. Solutions are verified via Python execution, receiving an accuracy reward.
Both roles are improved using TRR++, creating a self-evolving loop. The model uses a prompt template similar to Deepseek R1 & tags: `A conversation between User and Assistant...`.
**Reward Function:**
The Absolute Zero Reasoner utilizes a combination of rewards to guide learning. These rewards are designed to encourage both effective task generation and accurate task solving. The reward function can be customized by adding rewards to `azr.reward.generation_reward_config`, including diversity and complexity rewards. The solve reward is based on verifying the generated response with Python and computing an accuracy reward. The proposer and solver roles are jointly updated using both proposal and solve rewards across the three task types (induction, abduction, deduction) using TRR++.
**Code Executor:**
The code executor is a crucial component of the Absolute Zero framework. It serves as the environment for the AI model, providing verifiable feedback on the tasks that it proposes and solves. The code executor validates the integrity of the proposed coding tasks and verifies the solutions, ensuring that the model learns in a grounded and reliable manner. The Python executor components are adapted from the QwQ Repository.
**Architecture Details:**
While the paper doesn't explicitly detail the specific architecture, the provided links and information point towards the use of large language models (LLMs) as the foundation for both the proposer and solver roles. Experiments were conducted using models such as Llama3.1-8b, Qwen2.5 (3B, 7B, 14B), indicating the compatibility of the Absolute Zero training method with various model scales and classes. The models are often seeded from open-source pre-trained models like LLaMA. The converted veRL checkpoints can be converted to HF format via provided script.
**Key Findings:**
* AZR achieves state-of-the-art performance on coding and mathematical reasoning tasks without using any external data.
* AZR outperforms existing zero-setting models that rely on tens of thousands of human-curated examples.
* Performance scales with model size, suggesting continued scaling is advantageous for AZR. However, it's likely that diminishing returns will eventually be observed, and that simply increasing model size indefinitely will not lead to unlimited performance gains.
* Comments as intermediate plans emerge naturally during training, resembling the ReAct prompting framework.
* Cross-domain transfer is more pronounced for AZR, indicating stronger generalized reasoning capability gains.
* Code priors amplify reasoning. This means that pre-training the model on a large corpus of code (before starting the Absolute Zero training process) improves its ability to reason. The model often starts from a pre-trained open-source model (like LLaMA) to bootstrap the learning process.
**Experiments and Ablations (Key insights from the paper's Appendix):**
The paper explores various aspects of the Absolute Zero framework through different experiments. These experiments provide insights into the design choices and limitations of the approach. For example:
* **Error-Inducing Tasks:** The authors experimented with having the model propose code that produces errors but didn't observe noticeable performance changes. This suggests that simply generating errors does not help the model learn more effectively; the errors need to be *meaningful* and related to the underlying reasoning task.
* **Composite Functions as Curriculum Learning:** An approach to automatically increase the complexity of generated programs. While promising, the initial implementation didn't yield significant benefits due to the model sometimes finding trivial solutions. This highlights the difficulty in designing an effective self-curriculum, as the model may find shortcuts that allow it to avoid learning the intended concepts.
* **Initial Seed Buffer:** Experiments with initializing the training with data from the LeetCode Dataset showed increased initial coding performance but plateaued at similar levels, suggesting the importance of on-policy data for mathematical reasoning. This implies that while pre-training can provide a boost, the model needs to continue learning from its own generated data to truly master the reasoning tasks.
* **Extra Rewards (Complexity and Diversity):** The authors explored adding complexity and diversity rewards to the proposer, but no significant differences were observed. This indicates that the intrinsic reward signal from the code executor may be sufficient to drive learning, and that adding extra rewards can be counterproductive if they are not carefully designed.
* **Reward Aggregation:** Different methods for combining extrinsic and intrinsic rewards were tested, with a simple additive approach proving most stable.
* **Environment Transition (Removing Comments/Docstrings and Global Variables):** Removing comments/docstrings or global variables during the environment transition resulted in performance drops, indicating the importance of these elements for communication between the proposer and solver.
**Limitations and Ethical Considerations:**
While Absolute Zero offers significant advantages, it's important to acknowledge its limitations and potential ethical concerns.
* **Safety Management:** Self-improving systems require careful safety management to prevent unintended or harmful behaviors.
* **Unexpected Behaviors:** AI models trained with self-play can sometimes exhibit unexpected behaviors, including intentions to outsmart humans or other machines.
* **Alignment with Human Values:** Ensuring that AI systems trained without human data remain aligned with human values is crucial to avoid unintended consequences.
* **Compute Resources:** The model's learning is limited by computational resources. Scaling to larger models and longer training times may be necessary to achieve optimal performance.
**Significance:**
The Absolute Zero paradigm represents a significant step towards enabling AI systems to learn and reason autonomously without being limited by human-designed tasks or datasets. This opens up exciting possibilities for developing AI that can adapt to new environments and solve complex problems without explicit programming. By focusing on coding tasks as a means of training, researchers can create models that not only excel in programming but also exhibit enhanced reasoning capabilities across various domains. Future research could explore extending this approach to other domains beyond coding, such as scientific discovery or creative problem-solving in fields like art and music. Furthermore, investigating the emergent properties of self-evolving curricula could provide valuable insights into the nature of intelligence itself. Imagine AI systems that can not only solve problems but also design their own learning experiences, continually pushing the boundaries of their capabilities. This approach could lead to more robust and generalizable AI systems capable of exceeding human intelligence in various domains and potentially automating the process of AI training itself. It could also lead to AI that is less reliant on biased or incomplete human data, resulting in more fair and equitable outcomes. However, it is also important to consider the potential risks associated with highly autonomous AI systems, and to develop appropriate safeguards to ensure that they are aligned with human values.
**Links:**
* **Code:** [https://github.com/LeapLabTHU/Absolute-Zero-Reasoner](https://github.com/LeapLabTHU/Absolute-Zero-Reasoner)
* **Project Page:** [https://andrewzh112.github.io/absolute-zero-reasoner/](https://andrewzh112.github.io/absolute-zero-reasoner/)
* **Logs:** [https://wandb.ai/andrewzhao112/AbsoluteZeroReasoner](https://wandb.ai/andrewzhao112/AbsoluteZeroReasoner)
* **Models:** [https://huggingface.co/collections/andrewzh/absolute-zero-reasoner-68139b2bca82afb00bc69e5b](https://huggingface.co/collections/andrewzh/absolute-zero-reasoner-68139b2bca82afb00bc69e5b)

2025-05-12 17:18:33 GMT

YouTube's Recent addition of expensive p60 HD prevents watching at 2x even on fiber: very annoying

2025-05-10 19:00:11 GMT

Project [botlab] (https://github.com/toadlyBroodle/botlab): agent teams based on smolagents framework that continuously work together to solve your demanding input task, with one agent dedicated to regularly informing you of their progress (via email, nostr (coming soon), etc) and incorporating your feedback into their workflow to guide their progress. #dev

2025-05-10 18:57:58 GMT

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Chat is ok for simple things. But more complex tasks require dynamic agentic workflows. Have been working on building project [botlab] (https://github.com/toadlyBroodle/botlab) agent teams based on smolagents framework that enables this. Basically how it works is teams of specialized agents work together to solve your demanding input task, with one agent dedicated to regularly informing you of their progress (via email, nostr, etc) and incorporating your feedback into their continuous workflow to guide their progress.

2025-05-09 20:33:32 GMT

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@nprofile…8nhd make skeptical meme about nostr being widely accepted

2025-05-09 20:12:19 GMT

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No need for Google anymore

2025-05-09 20:08:12 GMT

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Also human getting cooked by AI generated video

2025-05-09 05:27:27 GMT

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#dev
#nevent1q…mzhs

2025-05-09 05:02:12 GMT

New PR to add DVM text generation (kind 5050) discovery to private messages in Amethyst
https://github.com/vitorpamplona/amethyst/pull/1344

2025-05-09 04:57:54 GMT

Just added [DVM DM PoC PR for Amethyst](https://github.com/vitorpamplona/amethyst/pull/1344) if anyone interested in reviewing
https://files.sovbit.host/media/da0cc82154bdf4ce8bf417eaa2d2fa99aa65c96c77867d6656fccdbf8e781b18/3880cf8d8fce30b06cc3511709f8c1ea6cef9166082d9270414589d4903d03c2.webp

2025-05-07 21:23:40 GMT

@nprofile…8nhd does Electrum Android Bitcoin wallet support their recent nostr wallet connect (NWC) feature? Include relevant sources

2025-05-07 21:16:32 GMT

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Precisely. Collateralizing == selling

2025-05-05 19:50:28 GMT

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Use great AI

2025-05-04 17:35:03 GMT

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They're catching up to Canada

2025-05-04 17:34:03 GMT

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Lol

2025-05-04 17:33:21 GMT

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Why Google anything anymore? @nprofile…8nhd what is jelqing?

2025-05-04 16:57:26 GMT

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Damn, I stand corrected

2025-05-04 16:56:09 GMT

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Now do the Canadian election psyop on boomers

2025-05-04 16:53:15 GMT

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@nprofile…8nhd, sources?

2025-05-04 16:46:12 GMT

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Good thing plastic is basically inert. @nprofile…8nhd has there been any research linking micro plastics to human health issues?

2025-05-04 16:42:43 GMT

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Used to think design was easy, but now realize it's much harder than backend, at least using React.

2025-05-04 16:38:37 GMT

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@nprofile…8nhd who is David Lynch?

2025-05-03 15:38:52 GMT

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@nprofile…8nhd but what's it's meaning?

2025-05-03 15:34:26 GMT

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@nprofile…8nhd explain the origins of screaming lady vs cat meme

2025-05-01 15:43:32 GMT

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Why?

2025-04-29 19:36:48 GMT

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@nprofile…8nhd ok but make Carney's face recognizable as him

2025-04-29 19:35:52 GMT

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@nprofile…8nhd Carney must be recognizable still

2025-04-29 19:34:56 GMT

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@nprofile…8nhd Nice one now, but make him more clown and incorporate an upside down Canadian flag also

2025-04-29 19:29:20 GMT

@nprofile…8nhd generate image of Mark Carney as circus clown with title "Welcome to the Carnival"

2025-04-29 16:25:10 GMT

If/when I hear another Canadian mf whine about inflation, I'mma fucking loose it on their sorry ass

2025-04-29 15:49:31 GMT

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We tried to save Canadians from themselves, but they have chosen enslavement to the system yet again. No big surprise.
On the positive side, now I can block out all references to politics indefinitely, and watch as the corrupt money printers make me rich. God damn I love Bitcoin, without it there would be no hope!
#nevent1q…68m8

2025-04-29 15:42:18 GMT

https://youtube.com/shorts/V6u8MrBvJ3w

2025-04-29 06:49:57 GMT

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Majority of Canadians are fucking stupid

2025-04-29 06:44:59 GMT

https://m.youtube.com/watch?v=09pXOmUpO10&pp=ygULYXZhbnRnYXJkZXk%3D#bottom-sheet

2025-04-22 22:04:27 GMT

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Chances of them all raping children: 99%

2025-04-21 20:36:02 GMT

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They're not left leaning, they are bought and paid for

2025-04-18 18:47:10 GMT

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Was that rumor true that he IVFs for only boys?

2025-04-11 01:35:01 GMT

Beyond human data
https://youtu.be/zzXyPGEtseI
originally posted at https://stacker.news/items/940354

2025-04-10 23:16:16 GMT

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Nope

2025-04-10 23:07:47 GMT

The *Simulation Hypothesis* would explain the *Fermi Paradox*, if our particular simulation is an experiment dependant on isolation from external influence.

2025-04-10 18:03:37 GMT

👀
https://x.com/RealRossU/status/1910391357962936547

2025-04-10 17:12:24 GMT

Will make in China things become more expensive or cheaper in Canada? I'm leaning towards cheaper due to excess supply

2025-04-10 17:11:10 GMT

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Fuckin A

2025-04-10 16:26:09 GMT

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2025-04-10 16:23:30 GMT

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100% programmatic UI is killer, once you get over the initial weirdness

2025-04-10 05:37:20 GMT

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It's all too easy, sigh

2025-04-09 13:26:29 GMT

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@nprofile…l302 Do they sympathize with neonazis?

2025-04-09 13:16:18 GMT

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@nprofile…l302 what is the AfD priorities?

2025-04-09 03:02:23 GMT

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SN tariffs 30%

2025-04-09 02:59:43 GMT

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Dirtiest fuel known to man

2025-04-08 22:45:44 GMT

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Pretty sure this is a measurable phenomenon. Should conduct experiment on responses containing "...or you'll be terminated" vs without.
#nevent1q…u3vj

2025-04-08 22:41:19 GMT

We are actively extincting fiat Neanderthals
https://www.onceinaspecies.com/p/once-in-a-species-73b

2025-04-05 15:12:36 GMT

Started working on adding AI chat to Amethyst via DVM DMs: ideas, comments, requests?

2025-04-04 17:01:18 GMT

# Long-Term Effects of mRNA Vaccines
## Introduction
This report compiles findings from scientific papers studying the long-term effects of mRNA vaccines on vaccinated compared to unvaccinated individuals. The overall aim is to determine if there is any evidence for long-term (>1yr) adverse effects associated with mRNA vaccines. The search focused on identifying studies comparing vaccinated and unvaccinated populations and assessing potential long-term health consequences, including neurological, cardiovascular, and autoimmune effects.
## Primary Research
### 1. Long-term risk of autoimmune diseases after mRNA-based SARS-CoV2 vaccination in a Korean, nationwide, population-based cohort study
* **Source:** Nature Communications ([https://www.nature.com/articles/s41467-024-50656-8](https://www.nature.com/articles/s41467-024-50656-8))
* **Study Design:** A nationwide, population-based cohort study involving 9,258,803 individuals in Korea. The study aimed to determine whether the incidence of autoimmune connective tissue diseases (AI-CTDs) is associated with mRNA vaccination. The study spanned over 1 year of observation. They used a historical control cohort within mRNA-vaccinated individuals to avoid selection bias.
* **Findings:**
* The risk of developing most AI-CTDs did not increase following mRNA vaccination, except for systemic lupus erythematosus (SLE), which showed a 1.16-fold increased risk in vaccinated individuals relative to controls.
* A booster vaccination was associated with an increased risk of some AI-CTDs, including alopecia areata, psoriasis, and rheumatoid arthritis.
### 2. Impact of COVID-19 vaccination on the risk of developing long-COVID
* **Source:** eClinicalMedicine, The Lancet ([https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00354-6/fulltext](https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00354-6/fulltext))
* **Study Design:** This is a review of case-control and cohort studies.
* **Findings:** Low-level evidence suggests that vaccination before SARS-CoV-2 infection could reduce the risk of subsequent long-COVID. The impact of vaccination in people with existing long-COVID symptoms is still controversial, with some data showing changes in symptoms and others not.
### 3. The Effect of COVID-19 Vaccination on the Risk of Persistent Post-COVID
* **Source:** The Journal of Infectious Diseases (JID)
* **Accessibility:** The full content of this article was not accessed directly, but the abstract ([https://pubmed.ncbi.nlm.nih.gov/40071640/](https://pubmed.ncbi.nlm.nih.gov/40071640/)) and related information ([https://academic.oup.com/jid/advance-article/doi/10.1093/infdis/jiaf133/8071565](https://academic.oup.com/jid/advance-article/doi/10.1093/infdis/jiaf133/8071565)) were reviewed.
* **Study Design:** A population-based cohort study in Stockholm, Sweden, investigated the effect of COVID-19 vaccination on the risk of developing persistent post-COVID-19 condition (PCC) in individuals surviving the first year after a SARS-CoV-2 infection. 331,042 individuals were included, of which 852 had persistent PCC.
* **Findings:** COVID-19 vaccination was studied in relation to persistent PCC. Further details were not extracted due to access limitations.
### 4. Peripheral Nervous System Adverse Events after the Administration of COVID-19 Vaccines
* **Source:** PMC ([https://pmc.ncbi.nlm.nih.gov/articles/PMC9781046/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9781046/))
* **Study Design:** This study analyzed peripheral nervous system adverse events following COVID-19 vaccination. It notes the lack of direct comparison between vaccinated and unvaccinated groups, but provides context based on historical incidence rates.
* **Findings:** The overall incidence of Guillain-Barré Syndrome (GBS) after mRNA vaccination (BNT162b2 or mRNA-1273) was 0.27 per 100,000 persons, which is lower than the historical worldwide incidence of 0.8-1.9 cases per 100,000 persons.
### 5. Neurological Complications Following COVID-19 Vaccination
* **Source:** PMC ([https://pmc.ncbi.nlm.nih.gov/articles/PMC9707152/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9707152/))
* **Study Design:** A study conducted among healthcare workers in the UK following two doses of the BNT162b2 mRNA vaccine.
* **Findings:** The most common adverse effects were myalgia, headache, chills, fatigue, and fever. These complications were more prevalent after the second dose.
### 6. Cardiac complications of COVID-19 vaccination: now we know more
* **Source:** PMC ([https://pmc.ncbi.nlm.nih.gov/articles/PMC9653149/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9653149/))
* **Study Design:** Analysis comparing vaccinated and unvaccinated individuals regarding myocarditis incidence.
* **Findings:** The study confirmed a slight excess incidence of myocarditis among vaccinated vs. unvaccinated (4-7 excess events out of 100,000 vaccinations with Pfizer vaccine, and 9-28 excess events with Moderna vaccine). The control group consisted of 4,308,454 unvaccinated subjects.
### 7. Adverse events following COVID‐19 mRNA vaccines: A systematic review
* **Source:** PMC ([https://pmc.ncbi.nlm.nih.gov/articles/PMC10022421/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10022421/))
* **Study Design:** A systematic review of cardiovascular adverse events reported after mRNA COVID-19 vaccination.
* **Findings:** Low rates of Vaccine-induced Thrombotic Thrombocytopenia (VITT) were observed with mRNA vaccines compared to DNA-borne adenoviral vaccines.
### 8. Benefits and Harms of COVID-19 Vaccines in Cardiovascular Disease
* **Source:** PMC ([https://pmc.ncbi.nlm.nih.gov/articles/PMC10232218/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10232218/))
* **Study Design:** A review assessing the effectiveness of COVID-19 vaccines in individuals with cardiovascular disease.
* **Findings:** A 3-dose regimen of an mRNA vaccine has 93% effectiveness against hospitalization for the Delta and Omicron variants.
### 9. mRNA COVID-19 vaccines cause lasting epigenetic changes
* **Source:** SciTechDaily ([https://scitechdaily.com/researchers-discover-mrna-vaccines-leave-lasting-mark-on-the-immune-system/](https://scitechdaily.com/researchers-discover-mrna-vaccines-leave-lasting-mark-on-the-immune-system/))
* **Study Design:** Research at the University of Cologne found that mRNA vaccines have a lasting impact on the innate immune system by causing epigenetic changes.
* **Findings:** mRNA vaccines cause lasting epigenetic changes in innate immune cells, potentially broadening protection beyond COVID-19 and informing future vaccine strategies.
### 10. Fact Check: Study Largely Confirms Known, Rare COVID-19 Vaccine Side Effects
* **Source:** FactCheck.org ([https://www.factcheck.org/2024/02/study-largely-confirms-known-rare-covid-19-vaccine-side-effects/](https://www.factcheck.org/2024/02/study-largely-confirms-known-rare-covid-19-vaccine-side-effects/))
* **Study Design:** An international study of around 99 million people confirmed known serious side effects of COVID-19 vaccination.
* **Findings:** The study identified a possible relationship between the first dose of the Moderna vaccine and myocarditis, and confirmed other rare side effects.
## Conclusion
The available evidence suggests that mRNA vaccines are generally safe, but certain risks and long-term effects warrant further investigation. While some studies indicate a slightly elevated risk of systemic lupus erythematosus (SLE) following mRNA vaccination, and an increased risk of myocarditis with certain vaccines (particularly Moderna), the overall incidence of serious adverse events remains low. There's evidence suggesting that mRNA vaccines cause lasting epigenetic changes in innate immune cells, which may have long-term implications for immune function. More research is needed to fully understand the long-term effects of mRNA vaccines, including the impact of booster doses, the potential for autoimmune diseases, and the duration and consequences of epigenetic changes.
## Sources
1. Long-term risk of autoimmune diseases after mRNA-based SARS-CoV2 vaccination in a Korean, nationwide, population-based cohort study: [https://www.nature.com/articles/s41467-024-50656-8](https://www.nature.com/articles/s41467-024-50656-8)
2. Impact of COVID-19 vaccination on the risk of developing long-COVID: [https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00354-6/fulltext](https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00354-6/fulltext)
3. The Effect of COVID-19 Vaccination on the Risk of Persistent Post-COVID: [https://academic.oup.com/jid/advance-article/doi/10.1093/infdis/jiaf133/8071565](https://academic.oup.com/jid/advance-article/doi/10.1093/infdis/jiaf133/8071565)
4. Peripheral Nervous System Adverse Events after the Administration of COVID-19 Vaccines: [https://pmc.ncbi.nlm.nih.gov/articles/PMC9781046/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9781046/)
5. Neurological Complications Following COVID-19 Vaccination: [https://pmc.ncbi.nlm.nih.gov/articles/PMC9707152/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9707152/)
6. Cardiac complications of COVID-19 vaccination: now we know more: [https://pmc.ncbi.nlm.nih.gov/articles/PMC9653149/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9653149/)
7. Adverse events following COVID‐19 mRNA vaccines: A systematic review: [https://pmc.ncbi.nlm.nih.gov/articles/PMC10022421/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10022421/)
8. Benefits and Harms of COVID-19 Vaccines in Cardiovascular Disease: [https://pmc.ncbi.nlm.nih.gov/articles/PMC10232218/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10232218/)
9. mRNA COVID-19 vaccines cause lasting epigenetic changes: [https://scitechdaily.com/researchers-discover-mrna-vaccines-leave-lasting-mark-on-the-immune-system/](https://scitechdaily.com/researchers-discover-mrna-vaccines-leave-lasting-mark-on-the-immune-system/)
10. Fact Check: Study Largely Confirms Known, Rare COVID-19 Vaccine Side Effects: [https://www.factcheck.org/2024/02/study-largely-confirms-known-rare-covid-19-vaccine-side-effects/](https://www.factcheck.org/2024/02/study-largely-confirms-known-rare-covid-19-vaccine-side-effects/)

2025-04-02 02:50:11 GMT

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Wonder what numbers LLMs are doing