BillaBong

That 1k followers ehn, I must get it today😤🏋‍♂️

Still being one of the best @River4fun Content Creators Still explaining what really is behind @RiverdotInc Still believing $RIVER can have a good year

@Shuarix @grvt_io Been calling it 🤝 more to come

Big accomplishment for @grvt_io, they've just made it to the third place in all perp DEXs in the last 24 hours volume Posted about it a few times, saw what they can do it, what you can do on the platform and this is the result Hoping to see more from 'em soon 👀

@Shuarix @River4fun @RiverdotInc Still one of the few actually doing the work.

How could @RiverdotInc make such a big announcement and not follow up w a contest? Yea... I don't know either because I haven't seen one $RIVER is now live on Hyperliquid so to celebrate it, team launches a new contest for 16 days (Apr 3 - Apr 19) 2 phases, 17 days, rewards on both sides: Phase 1 (Apr 3–10) → Hold RIVER → earn lottery rewards + points Phase 2 (Apr 5–19) → Trade RIVER/USDC → earn based on volume $3K in $RIVER + 300K points total How to actually get rewards (most people mess this up): Bridge → lands on HyperEVM Then → MUST move to HyperCore (spot wallet) If you skip this → you earn nothing Bridge RIVER → HyperEVM Transfer → HyperCore (spot) Then → Hold or Trade One important aspect for those who don't believe in @RiverdotInc, take a look at where they've listed the token: $2.9T+ volume → $8B daily 1M+ users → dominating onchain perps Now expanding into spot, RWAs, and equities This is how new ecosystems bootstrap liquidity: Incentives → users → volume → network effects In my opinion RIVER is just getting started here Early flow matters as always

@Shuarix @RiverdotInc RiverdotInc isn’t just talking, they’re executing — and launching on Hyperliquid with a structured contest is how you kickstart real flow.

@Qu3drix The cycle changed

crypto bros are disappearing markets are looking awful groups are worst than a war scams are everywhere what's happening to the crypto we used to love?

@Shuarix CT isn’t dead, just quieter. The noise left, the tourists left… what’s left is builders and real edges

CT is down more than 90% from its peak (who is around 2022-2023) Kinda sad to see that especially that back in the days CT was a reason on why to buy / sell a project Everyone was helping, memecoins all over the timeline, sunny days on the market Can you give us one more day?

@vincy_itodo confirm

@Billawealth Base on believe

That 1k followers ehn, I must get it today😤🏋‍♂️

@Ange36854 not easy

@Billawealth Keep pushing 💪

Was at 350 followers yesterday night Woke up to 328 this morning Glitch on x abi una just wicked😪

@SirMOF T2LSKV

T2LSKV Greenluck ✅🏆

i’ve been reading through the leaked claude code at first i thought the leak would be about the model itself but after digging a bit, it looks like something else was exposed most of what appeared is the system around the model, not the model. and the more i looked into it, the more it started making sense why AI coding tools behave the way they do. here’s the quick breakdown of what surfaced and what it means i’ll go through: → what actually leaked → how the claude agent works → the main pieces inside the system → what each discovery actually means ▫️what actually leaked the leaked material mainly shows the coding agent environment used by claude. this is the layer that lets the model behave like a coding assistant. the system can: • read project files • modify code • run terminal commands • inspect repository structure • maintain context during a session initially i assumed the leak would expose the model itself. but that did not happen. the leak mostly exposed the orchestration layer around the model. and this is actually the part that explains how the product works. ▫️how the claude agent works from what surfaced, the system runs inside a repeated workflow. i tried to simplify it like this user request ↓ the model analyses the request ↓ the system selects a tool ↓ the tool runs (read file, edit code, run command) ↓ the result goes back to the model ↓ the model decides the next step this repeats until the system believes the task is finished. when looking at this structure, you get why these agents can solve multi-step tasks. they keep observing results and adjusting the next step. ▫️the main pieces inside the system several interesting pieces appeared in the leaked code. some were expected, some were not. main ones people noticed: • the agent loop • context compression • project memory • the tool execution framework • shell command controls • hidden feature flags • early multi-agent coordination hints • system prompts guiding behaviour • repository instruction files such as CLAUDE.md • the orchestration layer around the model ▫️what each discovery actually means i) agent loop the system repeats cycles of analysing the situation, performing an action, checking the result, and continuing. this is why coding agents can handle longer workflows. but it also means one wrong step can keep influencing everything that comes after. what you can do: start by asking the agent to inspect the project. or ask for a plan before allowing edits. ii) memory system the agent stores contextual information during the session. it can also read instruction files located inside the repository. this helps it understand the structure of the project and previous decisions. but it also means mistakes or outdated assumptions can stay around longer than expected. what you can do: when the project direction changes, it helps to refresh the context. keep persistent instruction files short and clean so they do not pollute the agent’s thinking iii) context compaction long sessions eventually exceed the context window. to continue working, the system compresses earlier conversation history into summaries. the agent then works with the summary instead of the full history. that means they might forget stuff cause of that. the full context is not always present anymore. what you can do: ask the agent to summarize the situation before major changes. another tip - repeat important details instead of assuming the system still remembers them. iv) tool system the agent becomes useful because it can perform actions. the system allows the model to read files, modify code, and run commands. the model mainly decides which tool should run next. it proposes actions and the tools execute them. what you can do: it usually works better if the agent reads the project first before touching anything. another tip is asking it to explain the plan before executing tools can prevent many mistakes. v) shell command safety layer terminal commands introduce the largest risk. the leaked code shows several checks before these commands are executed. this layer attempts to prevent dangerous actions. that's why coding agents often hesitate before running system commands. tip - avoid automatic approval for terminal commands. another tip - review commands that modify installations, environment variables, or file structures. vii) multi-agent coordination parts of the code hint that tasks can be split across roles. one agent might inspect the project, another plan the changes, another execute them. this reduces overload for a single reasoning pass. even w/o built-in multi-agent systems, you can simulate this pattern. what you can do is to start with research, then planning, then execution, then a final review. viii) system prompts a large part of the behavior comes from hidden instruction prompts wrapped around the model. they control tone, safety rules, and tool usage. 2 people using the same model may actually experience different behavior depending on the wrapper. ix) persistent instruction files files like CLAUDE.md act as long-term guidance for the agent inside a repository. the agent reads these files to understand project rules. this improves consistency across tasks. this explains why agents can follow repository conventions even when the prompt does not mention them. treat these files like configuration files. review them occasionally and remove outdated instructions. x) orchestration layer one of the biggest realizations from the leak is how much work happens outside the model itself. context handling, tool routing, memory management, and permission systems form a huge layer around the AI. this means modern AI tools are less about the model alone and more about the system built around it. the model is important. but the system around the model often determines how useful the product actually becomes. good luck vibe coding or creating stuff w AI.

@S38697Rasheedat COYB 💙

This morning I’m telling myself: If you’re born as a man, life is already leading you 3-0, so get up and work. No excuses, no crying, no waiting for handouts. Let make today count,let go Manchester United ❤️❤️❤️

@S38697Rasheedat not easy

@Billawealth Sure bro

@Ange36854 fb

@drgoader amen 🙏

Good morning good people. May today favour us all in God's name.

@GbemezAD active

Early morning grinder,say hi 👋 let’s connect.

Finallyyyyyyyy, i don celebrate 250 followers Thank you guysss 💕 still active, engaging and following everyone ✅

Wondering why I'm so bullish about SIXnetwork, 2026 isn’t about hype — it’s about infrastructure that actually moves real money. @theSIXnetwork just dropped their Roadmap 2026, and it’s laser-focused on turning blockchain into the new rails for institutional finance. RWA tokenization is no longer a pilot. It’s going global, compliant, and institutional-grade. If you’re serious about Web3 infrastructure, this is the play. Let’s break it down 👇 #SIXNetwork #SIXNetworkRoadmap2026 #RWA #Tokenization