Fabio Makita ✓
@AkitaOnRails · Montana, USA · joined 10 Apr 2007
Agile Senior Vibe Coder 🦅😂 Assine a Newsletter The M.Akita Chronicles https://t.co/2wEqX99wye 🇺🇸🇯🇵🇮🇱 Gritando pras nuvens
91 596Followers
417Following
59 966Posts total
188.6KViews on collected posts
Derniers posts
Recherche de nouveaux posts…
Probably by tomorrow I will release ai-memory version 2.4. This is possibly the largest new feature set I released, tons of goodies!
Check the changelog: https://t.co/yzlteh3ORe
If you're a contributor, please test now. Bug fixes will continue to be tested and merged until I ht
4.4K views · 78 likes
· 0 reposts · 2 replies
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Só porque eu estava falando de mastersystem hoje, sai um documentário da Digital Foundry. Recomendo.
1.9K views · 13 likes
· 0 reposts · 2 replies
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Como o hype de Jev continua, deixei um agente fazendo uma pesquisa pra mim. A conclusão é a mesma, é um bom classificador.
Agora acho que estou entendendo o hype e o que todo mundo estava fazendo errado. Em vez de fazer a LLM codificar um classificador pro seu use case, muita ht
35K views · 453 likes
· 17 reposts · 20 replies
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A pessoa me fala "e aí, já fez algo útil" e eu dobro a aposta e começo mais 3 projetos completamente inúteis.
Só porque eu posso kkkkk 🤣
4.6K views · 94 likes
· 0 reposts · 7 replies
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Resolvi iniciar mais 3 novos pequenos projetos de retrogames:
- Recriar Super Mario 35 (que foi temporário e não existe mais) mas como eu não gosto de jogar multiplayer, quero jogadores IA pra jogar comigo.
- Terminar a lista de jogos de Game Gear que ainda não foram https://t
15.8K views · 79 likes
· 2 reposts · 10 replies
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The new DF Retro Super Show is live, revisiting the Sega Master System in depth, with a special guest appearance from @GameSack! https://t.co/mfwFdRX9PP
17.9K views · 63 likes
· 7 reposts · 4 replies
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@AkitaOnRails This solves a real dorm-room pain point: switching models usually means re-teaching the whole repo. Shared memory plus readable docs feels much more durable than hidden state.
708 views · 3 likes
· 0 reposts · 1 replies
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@AkitaOnRails the wiki angle is the part i care about. memory i can read in the repo beats memory buried in embeddings i can't check.
535 views · 2 likes
· 0 reposts · 1 replies
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@AkitaOnRails tá, isso é basicamente fazer um arquivo pra ambos agentes ficarem lendo dele, isso?
291 views · 1 likes
· 0 reposts · 1 replies
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AI-MEMORY is the premiere Long-term memory for AI coding agents. Quit Claude Code mid-task, start Codex in the same directory, continue without re-explaining the architecture, the failed approaches, or the open questions. With proper memory consolidation, Wiki-based documentation
45.2K views · 336 likes
· 27 reposts · 30 replies
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@AkitaOnRails Isso vai envelhecer igual leite deixado no tempo.... kkkkk
148 views · 1 likes
· 0 reposts · 0 replies
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@AkitaOnRails E se um dia eu conseguir gastar token o suficiente pra ferver um lago inteiro com o calor do datacenter, eu vou gastar com o maior prazer do mundo. É minha vingança pessoal contra quem me obrigou a usar canudo de papel que desmancha na boca por tantos anos. 😂😂😂😂😂😂
871 views · 9 likes
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@AkitaOnRails Cara esse post remete exatamente ao curso que criei. Gosto de testar coisas novas e provavelmente vou testar esse classificador de json mas a galera é muito emocionada. Não a toa o mercado é traduzido entre o urso e o touro.
937 views · 3 likes
· 0 reposts · 0 replies
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Algumas pessoas, aqui, no blog, etc começaram a aparecer me perguntando "Akita, já testou TypeSafe - o que acha deles?"
Isso é red-flag imediato pra mim.
Então resolvi escrever mais um artigo explicando como eu lido com assuntos assim:
"Por que coisas como TypeSafe IA não me
60.4K views · 140 likes
· 4 reposts · 19 replies
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Face aux comptes de taille comparable
14 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. portée ordinaire pour sa taille, réaction plus faible que la moyenne.
Vues médianes3 157ce compte980médiane pour 10K–100K
Portée, %3.45%ce compte3.84%médiane pour 10K–100K
Engagement, %0.68%ce compte1.53%médiane pour 10K–100K
| Indicateur | Ce compte | Médiane pour 10K–100K | Rapport |
|---|---|---|---|
| Vues médianes par post | 3 157 | 980 | 3.22× |
| Portée (vues ÷ abonnés) | 3.45% | 3.84% | 0.90× |
| Taux d'engagement | 0.68% | 1.53% | 0.45× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
60.4K16 Sep
937
871
14817 Sep
45.2K19 Sep
291
53520 Sep
708
17.9K
15.8K
4.6K
35K
1.9K
4.4K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.28%16 Sep
0.32%
1.03%
0.68%17 Sep
0.87%19 Sep
0.69%
0.56%20 Sep
0.56%
0.43%
0.58%
2.20%
1.41%
0.80%
1.81%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.53%.
What the audience does
Likes66.5%1 275 in total
Reposts3.0%57 in total
Replies5.1%97 in total
Quotes0.6%12 in total
Bookmarks24.8%475 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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