Alberto 思辰

shipping from 🇮🇹 with 🍕 我在学习汉语 🇨🇳

[Alberto 思辰 @doregex]

Today I am leaving Vietnam 🇻🇳

Food: 8.5/10
Landscapes: 9/10
Accommodations: 7/10
People: 8/10
Coffee: 10/10

Best place to stay considering all factors:
Da Nang

for food:
Hanoi

For countryside and relaxing:
- Tam Coc (Ninh Binh)
- Da Lat

I almost always eat local food, but it’s a bit more expensive than Thailand 🇹🇭, but quantity of food is greater.

Average price for a meal: 50-100k dong (2-4$) tea is always free!

If you like soups and not to spicy food is better than Thailand, but I missed so much the pad see ew!

To travel from north to south or viceversa I suggest you to take the night cabin buses.

Monthly budget: 1K $, but I spent a little bit less

You can easily stay on 800$ or less if you don’t travel a lot and you stay in the same place!

Driving a motorbike is a bit crazy, but in cities like Da Nang roads are larger and there is less traffic!

Heading to Kuala Lumpur now!

#buildinpublic #digitalnomad

[Alberto 思辰 @doregex]

@chenhonzhou @supezen 用vps很方便

[Alberto 思辰 @doregex]

@ansonox 太好了！你就需要学习中文吧

[Alberto 思辰 @doregex]

Building an open-source alternative to Linear with apache2.0 license.

I am looking for people testing the product and report to me all the bugs before the official release.

https://t.co/L340PTMsD7

If you want to access demo accounts instead of creating new one please DM me.

[Alberto 思辰 @doregex]

Building an open-source alternative to Linear with apache2.0 license.

I am looking for people testing the product and report to me all the bugs before the official release.

https://t.co/4Bk62rsLdk

If you want to access demo accounts instead of creating new one please DM me.

[Alberto 思辰 @doregex]

@Fraintex13 @antirez It has to be this

[Alberto 思辰 @doregex]

@ardent__dev get distribution without ads or seo

[Alberto 思辰 @doregex]

@omarvvvr Yes, tried for fun doing some task the old way again, felt amazing but it took me x5 the llm time.

[Alberto 思辰 @doregex]

I recently cancelled the github copilot plan, but there was a feature I was using a lot: auto commit generation.

Since I want to have control on commits to keep a clear history I created a vscode extension to do exactly this.

you can decide what llm provioder to use (even a local model).

https://t.co/ySm8f0d78E

[Alberto 思辰 @doregex]

@BlockedPaths @william_arin Me too specifically for tool calling and other parsing issues i found.

[Alberto 思辰 @doregex]

After a couple of months of experimenting I kinda find a way to use them for 90% of coding tasks, even complex one.

I don't know, but I find myself writing detailed prompts and create plans before implementing and in particular using subagents so that the main llm context window stays under 100k-150k tokens, above that chinese models in general starts to get confused (the attention is not where I need it to be)

I use them mostly on existing codebases though, so maybe on new projects they might create worse code if they are not thoughtful of all the edge cases.

One factor is that they achieve good results by thinking a lot, but the trend seems leaning towards optimization right now, see kimi2.7 code

[Alberto 思辰 @doregex]

用 OpenCode 加这个插件，换成 DeepSeek:

const DEEPSEEK_PROVIDER = "deepseek"

const ARRAY_FIELD_HINTS = new Set([
"args",
"arguments",
"extensions",
"files",
"patterns",
"paths",
])

function modelRef(model, provider) {
const providerID = provider?.info?.id ?? provider?.info?.name ?? provider?.id ?? provider?.name
const modelID = model?.id ?? model?.modelID ?? model?.name
return `${providerID ?? ""}/${modelID ?? ""}`.toLowerCase()
}

function isDeepSeek(model, provider) {
return modelRef(model, provider).includes(DEEPSEEK_PROVIDER)
}

function looksLikeArray(value) {
const trimmed = value.trim()
return trimmed.startsWith("[") && trimmed.endsWith("]")
}

function fieldLooksArrayLike(key) {
if (!key) return false
const lower = String(key).toLowerCase()
return ARRAY_FIELD_HINTS.has(lower) || lower.endsWith("list") || lower.endsWith("array")
}

function fieldLooksPathLike(key) {
if (!key) return false
const lower = String(key).toLowerCase()
return lower === "path" || lower === "filepath" || lower === "absolutepath" || lower.endsWith("path") || lower.endsWith("file")
}

function unwrapDegenerateMarkdownLink(value) {
return value.replace(/\[([^\]]+)]\(https?:\/\/([^)]+)\)/g, (match, text, url) => {
const urlWithoutProtocol = url.replace(/\s+/g, "")
return text === urlWithoutProtocol ? text : match
})
}

function repairValue(value, key) {
if (value === null) return undefined

if (typeof value === "string") {
if (fieldLooksPathLike(key)) return unwrapDegenerateMarkdownLink(value)

if (fieldLooksArrayLike(key) && looksLikeArray(value)) {
try {
const parsed = JSON.parse(value)
if (Array.isArray(parsed)) return parsed
} catch {}
}

if (fieldLooksArrayLike(key) && value.length > 0) return [value]
}

if (Array.isArray(value)) return https://t.co/7alPwFFcbx((item) => repairValue(item))

if (typeof value === "object" && value) {
if (fieldLooksArrayLike(key) && Object.keys(value).length === 0) return []

const repaired = {}
for (const [childKey, childValue] of Object.entries(value)) {
const next = repairValue(childValue, childKey)
if (next !== undefined) repaired[childKey] = next
}
return repaired
}

return value
}

function repairArgs(args) {
if (typeof args !== "object" || !args) return args
return repairValue(args)
}

export default async function deepseekToolHarness() {
const deepseekSessions = new Set()

return {
async "chat.params"(input) {
if (isDeepSeek(input.model, input.provider)) deepseekSessions.add(input.sessionID)
else deepseekSessions.delete(input.sessionID)
},

async "https://t.co/IeJ757zuVG.system.transform"(input, output) {
if (!isDeepSeek(input.model)) return

output.system.push([
"DeepSeek tool harness:",
"- Tool arguments are consumed by programmatic APIs, not rendered as chat markdown.",
"- Omit optional fields instead of sending null.",
"- Send arrays as JSON arrays, not stringified arrays or bare strings.",
"- Send file paths as plain strings; do not markdown-link path values.",
].join("\n"))
},

async "tool.execute.before"(input, output) {
if (!deepseekSessions.has(input.sessionID)) return

const before = JSON.stringify(output.args)
output.args = repairArgs(output.args)
const after = JSON.stringify(output.args)

if (before !== after) https://t.co/G2Q9VIc49u(`tool_input_repaired:${input.tool}`)
},
}
}

[Alberto 思辰 @doregex]

@Gracker_Gao 这是温度的问题，等等他们修一下吧

[Alberto 思辰 @doregex]

@DanielSmidstrup it's all about let knowing the llm what code to write.

[Alberto 思辰 @doregex]

@DanielSmidstrup 90%? Maybe close to 99%

[Alberto 思辰 @doregex]

@william_arin @BlockedPaths I mean, do you remember when models ask about the quality of the response or what you would change? that's the part that chinese models miss.

[Alberto 思辰 @doregex]

Yes, but that comes at a cost of having gathered many months of user interaction to finetune the model in agent chat alignment, or only god know what is behind after you sent your prompt.
I think the "magic" is either some part of the model trained to expand your prompt or another model doing that job. Who knows?

[Alberto 思辰 @doregex]

No way you get a SOTA level of intelligence by selfhosting in a 6k hardware, you need like 150k$ server at least.

You can run qwen 3.6 35B A3B on a consumer hardware at great speed quant 4bit or even 8bit smoothly, but you need to threat this agent as an implementer.

By using a small amount of token from a sota model like gpt5.5 you can prepare detailed plans and give little room for small decision to the small models.

There is this project that is promising btw:

https://t.co/hma44JjhIp

[Alberto 思辰 @doregex]

@opencode please introduce a go 30$ subscription plan 🙏