Hey welcome to Hashlink! Catch us on Bullish on Base spaces with @based_elnen today! Main App: https://t.co/G3Dsqu1C7E Hashlink CLI: https://t.co/lFLMHRIaFU Telegram: https://t.co/g0kHVdBWA8 Dexscreener: https://t.co/HCsyM1xHrB CA: 0x870cA00dBb2789F37eF684D6dE4f7BB9a6b3Cb07

See you guys on Thursday!

Cooking another banger episode of "Bullish on Base." 🟦 We're gonna discuss: - AI & innovative agents - Market sentiment - Emerging AI narratives ▫️ Cohost - Justin @DecentralBros_ 🎙 We might see: ▫️ @santisairi $SAIRI - @santisiri ▫️ $ROBOTMONEY - @LexSokolin Confirmed

https://t.co/5eEqJzCKW1

10. Crypto is part market, part internet culture, part collective hallucination. if we want AI agents to operate well here, they need to understand all 3. that is what we are building with Hashlink CLI, and you can use it FOR FREE! https://t.co/lFLMHRIaFU

9. if an agent only sees candles and volume, it misses half the game. if it can also understand: - what people are obsessed with - what meme is spreading - what community language is forming - whether attention feels genuine or forced then it can make much better decisions.

8. the important part is this: we are not trying to make the AI agent sound funny. we are trying to give the agent better context. because in crypto, markets move on more than just price. they move on: narrative, social proof, vibe, timing, culture

7. once we collect the raw context, we use Grok via @AskVenice for the heavier interpretation layer. why? because Grok is ridiculously good at: - X native language - memes & online culture Turning noisy crypto chatter into something an agent can actually use.

6. we also check Google Trends to get a rough signal for how hot or cold a topic is outside pure chart action. sometimes the chart looks boring but attention is quietly building. sometimes the chart pumps but the broader narrative is already fading. that difference matters.

5. but website copy alone is not enough. if you want an agent to understand a token properly, you need the meme layer too. so we pull extra context from places like: - X - 4chan - Reddit because that is where a lot of crypto narrative actually lives.

4. we scrape the website, summarize it into clean format. this part does not need an expensive model. dirt cheap models like Qwen 3.5 9B on @AskVenice are good enough for: cleanup, summarization, formatting, turning messy site into something your agent can easily consume.

3. our starting point is actually the main Hashlink app. we already collect and organize the important project URLs there: - website - socials - project links - token context so the CLI can reuse that foundation instead of starting research from zero every time.

2. most crypto agents are still too literal. they can read price action. they can parse basic token data. but they still miss the actual internet context: meme & pop reference. narrative heat, whether a coin feels dead, ironic, cult-like, or suddenly hot.

1. How do you teach an AI agent to understand crypto twitter memes without burning money on expensive models for every step? for our Hashlink CLI + agent skill, we use @AskVenice as the LLM gateway so we can route different jobs to different models.

What's your biggest financial mistake in the last cycle? Story time. (bet nothing can top the $49M slippage tho).

Hashlink ( $HLINK ) is now live on @coingecko! https://t.co/ClFleQgyxD

Most people will ignore this post. But not you. You're GMI anon. We have faith in you.

https://t.co/HNlO4uQn5G

We're trying to solve a problem in our token research pipeline, quite a major one. How do you explain a meme and it's context to an AI agent? How can it decide if it's really trending and worth buying? There's no other model better at this job than @grok. (We tested a lot of

That's it. We're pivoting. OnlyClaws lfg