u/GeorgeHadjisavvas

Κάθε βδομάδα δημοσιεύονται δεκάδες δημόσιοι διαγωνισμοί στην Κύπρο μέσω TED (Tenders Electronic Daily). Οι περισσότεροι περνούν απαρατήρητοι.

Εδώ είναι οι μεγαλύτεροι που άνοιξαν 28 Σεπτ μεχρι 2 Οκτ 2026:

€16.6M – Διαχείριση ιατρικών αποβλήτων (ΟΚΥΠΥ)
Συλλογή, μεταφορά, επεξεργασία και τελική διάθεση ιατρικών αποβλήτων. Deadline: 26/10

€1.72M – Ελαστομερείς αντλίες για θαλασσαιμία (ΟΑΥ)
Αντλίες μίας χρήσης για υποδόρια συνεχή έγχυση Deferoxamine. Deadline: 16/10

€1.4M – Ασφαλιστική κάλυψη ΚΟΑ
12 μήνες + δικαίωμα ανανέωσης 4 χρόνια. Deadline: 04/11

€940K – Υποχλωριώδες νάτριο (Τμήμα Αναπτύξεως Υδάτων)
Προμήθεια NaOCl για τις ανάγκες του ΤΑΥ. Deadline: 13/10

€785K – Χώρος στάθμευσης Πανεπιστημίου Κύπρου
Διαμόρφωση parking Βιβλιοθήκης "Στέλιος Ιωάννου". Deadline: 20/10

675K – Καυστική σόδα για ΑΗΚ
Caustic Soda 50% για Reverse Osmosis στο Βασιλικό. Deadline: 22/10

€672K – Παρακολούθηση επικονιαστών (Τμήμα Περιβάλλοντος)
Μελέτη ποικιλότητας εντόμων-επικονιαστών. Deadline: 13/10

Αν ενδιαφέρεστε για δημόσιες συμβάσεις, το Symvaseis.CY μαζεύει αυτόματα όλους τους διαγωνισμούς και Ανάθεσεις καθημερινά και στέλνει ειδοποιήσεις μέσω Telegram group για κάθε νέο διαγωνισμό.

Κάθε βδομάδα δημοσιεύονται στο TED (η πλατφόρμα δημοσίων προμηθειών της ΕΕ) δεκάδες διαγωνισμοί που αφορούν Κύπρο , απλά κανείς δεν τους βλέπει γιατί είναι θαμμένοι ανάμεσα σε 2.400 ανακοινώσεις τη μέρα από 27 χώρες.

Αυτοί ήταν οι 3 μεγαλύτεροι της τελευταίας εβδομάδας (1–7 Σεπτεμβρίου):

Ανάπτυξη και λειτουργία χρηματοοικονομικών IT συστημάτων | €700.000.000
Η Ευρωπαϊκή Επιτροπή (DG Budget) ψάχνει ανάπτυξη, υλοποίηση, συντήρηση και λειτουργία των χρηματοοικονομικών IT συστημάτων της. Αν έχετε ή δουλεύετε σε εταιρεία τεχνολογίας που κάνει enterprise software, system integration ή IT consulting ... αυτός είναι από τους μεγαλύτερους IT διαγωνισμούς που βγάζει η Επιτροπή.

Προμήθεια Evinacumab "Evkeeza" | €698.057
Διαγωνισμός του Υπουργείου Υγείας Κύπρου για ενέσιμο Evinacumab. Αφορά εταιρείες φαρμακευτικής διανομής και ιατρικών προϊόντων.

Χρηματοοικονομική και λογιστική υποστήριξη | €265.000.000
Ακόμα ένας διαγωνισμός της Ευρωπαϊκής Επιτροπής (DG Budget) .. λογιστικές, ελεγκτικές και συμβουλευτικές υπηρεσίες. Κλασικά Big 4 territory, αλλά μικρότερες εταιρείες μπορούν συχνά να συμμετέχουν ως υπεργολάβοι ή μέλη κοινοπραξίας.

Αν ενδιαφέρεστε για δημόσιους διαγωνισμούς, έχουμε μαζέψει όλους τους ανοιχτούς σε ένα site φιλτραρισμένους για Κύπρο: Symvaseis.CY .. υπάρχει και chat group μέσα για όποιον θέλει να τους λαμβάνει αυτόματα κάθε μέρα

Been running a few agentic workflows in production and hitting a coordination problem I don't see discussed much.

Individual approval flows are easy... one agent, one Slack ping, one thumbs up. Solved a hundred times.

Where it breaks down for me is when you have multiple agents running across multiple workflows, all needing human review for different things. Suddenly you've got:

• Approval requests spread across Slack channels, emails, and custom dashboards
• No single place to see what's actually pending right now
• Different response mechanisms for different agents (button click here, reply "yes" there, click a link somewhere else)
• No consistent audit of who approved what and when
• Requests getting missed because nobody's actively watching one specific channel

The workaround I've seen most often is "just pipe everything into one Slack channel" which sort of works until the channel becomes noise and people stop reading it.

Curious how others in this sub are handling it:

• Are you consolidating approvals into a single interface, or letting each agent handle its own?
• If consolidating: what are you using? Custom-built, existing tool, or hacked together in something like Retool/n8n?
• How do you handle timeout/expiry when an approval sits pending for too long?
• Are you tracking approve/reject decisions anywhere for later audit?

For context: I've been building an approval layer around this exact problem (singlee inbox approval across workflows) so I'm biased about the "consolidate" side of it, but genuinely curious what setups people have converged on and what's working in the real world.

i have been monitoring Cyprus public digital services through a small project and it looks like the Nicosia Municipality website has been unreachable from the monitor for roughly the past day.

CyObserver has been consistently recording failed checks and currently shows the service as unavailable.

Before treating it as an actual outage, I'm curious: can anyone else access the website normally? Especially interested in results from different ISPs/mobile networks.

Im building CyObserver to independently track the availability and reliability of Cyprus public platforms over time, so reports like this also help me distinguish a genuine outage from something like regional/IP blocking.

Site : nicosiamunicipality.org.cy/

been building automation workflows for a while, and the mental shift that actually mattered wasn't a tool or a framework, it was realizing "can I automate this" and "should this run without me looking at it" are two completely different questions.

here is the way i run every workflow action :

Can I undo it in under 5 minutes?
Does it touch money or customer data?

If both answers are good (reversible, low stakes) .. let it run fully automated. Logging, tagging, internal notes, anything you can fix in two clicks if it's wrong.

If either answer is bad (irreversible, or touches money/data) ... that action needs a human to glance at it before it fires. Not necessarily every time forever, but at least until you trust the pattern.

Where this bit me: I had a workflow auto-processing refunds based on a support ticket classifier. Worked great for weeks. Then one ticket got misclassified and $400 went out before I even saw it. Nothing was "broken

the automation did exactly what I built it to do. I just never should have let that specific action run unattended.

Since then I've gotten a lot more deliberate about separating "things that can just run" from "things that need a checkpoint." Ended up building a small tool for this myself (Decree, an approval API you can drop between an action and its execution) because I got tired of hacking together Slack pings for every workflow that touched something sensitive.

Question for anyone running automations in production:

do you have any automations that run fully hands-off touching money, customer data, or anything hard to reverse? Or do you keep a human in the loop for those by default? and if so how you keep human in the loop for multiple workflows without decentralized slack/team messages?

Been running a few AI agent workflows in n8n and started manually going through each one asking: if this action goes wrong, how bad is it?

Ended up with a simple framework that's been useful:

Step 1 — List every action your agent can trigger. Not the whole workflow, just the individual actions (send email, update record, create refund, delete row, etc).

Step 2 — For each one, ask two questions:

• Can I undo this in under 5 minutes?
• Does it touch money or customer data?

Step 3 — Sort into three buckets:

• Reversible + low stakes → let the agent run it automatically
• Irreversible OR touches money/data → needs a human checkpoint before it fires
• Both → definitely needs a checkpoint, ideally with a short deadline so it doesn't just sit there

Doing this exercise on my own workflows was uncomfortable... I had way more unprotected irreversible actions than I expected.

I tried to make this as a tool that runs for against an n8n workflow export, since doing it manually for every workflow got tedious. ..

how others are thinking about this ?? are you gating any actions in your workflows right now, or trusting the agent end to end?

Been running n8n workflows with AI agents (OpenAI, Anthropic) connected to live systems for a few months. Some things I learned the hard way that might save others time:

The AI will eventually do the wrong thing. Not if, when. A prompt that works 99% of the time still means 1 out of 100 actions hits your CRM, inbox, or payment system with bad data. At scale that adds up fast.

Separate reversible from irreversible actions. Let the agent auto-execute things you can undo (drafting a doc, logging a note). Gate everything you can't easily undo .. sending emails, updating billing, deleting records. That one filter cuts most of the risk.

One inbox for all pending actions saves your sanity. When you're running 5+ workflows with AI nodes, approval requests scatter across Slack threads and emails. Having a single place to review what every agent wants to do across your workspace is a game changer.

Auto-allow the boring stuff, require approval for the rest. Not every action needs a human. Set simple policies ... if the action is low-risk, let it through. If it touches customer data or money, hold it for review.

Log everything. When something goes wrong (it will), you want to know exactly what the agent proposed, who approved it, and when. That audit trail is also becoming a legal requirement in some markets .. the EU AI Act just made human oversight enforceable this month.

What does your production setup look like? Curious how others are handling the trust problem with AI agents.

Most agent demos show the happy path: agent gets a goal, plans, executes, done. The part nobody shows is what happens when the agent needs a human to say "yes, go ahead" before it does something irreversible .... spend money, email a customer, push to prod, delete data.

I've been building that approval layer and I keep hitting the same walls. Wanted to compare notes, because it feels like everyone's reinventing this quietly and nobody's really talking about it

The rubber-stamp problem. The whole point of a human gate is judgment. But the moment volume goes up, the human stops reading and just clicks approve. Now you've added latency and kept all the risk ... worst of both worlds. An approval step that always gets approved is just a slow no-op.

2. Where do you even put the boundary?: Too many gates and the agent is useless and yes .... you'd be faster doing it yourself.Too few and the one time it mattered, there was no gate. I haven't found a clean rule for "this action needs a human." Right now I'm gating on irreversibility + blast radius, but it's fuzzy and I second-guess it constantly.

3. Giving the human enough to decide, but not a wall of text. When the agent stops and asks, the human needs context: what's it about to do, why, what happens if it's wrong. Too little and they can't judge. Too much and they don't read it (see #1). Getting this down to a "5-second good decision" is way harder than it sounds.

4. The agent is waiting and the human is asleep. This one wrecked me. The agent hits a gate at 2am .. now what? Block the whole workflow? Time out and skip (dangerous)? Queue it and hold state until morning? Every option has a failure mode, and holding agent state cleanly across a long pause is its own headache.

5. Approval ≠ understanding. People approve things they didn't fully grasp the downstream effect of. Then when it goes wrong, "well, you approved it" is technically true and completely useless for anyone.

My rough answers right now: gate on irreversibility, keep the approval payload tiny and structured, make timeouts fail closed (block, never skip), log everything for audit. But honestly it all feels held together with tape.

So ..... how are you handling this in your own setups?

• What triggers a human gate vs. letting the agent run?
• Slack button, dashboard, email, something else?
• What do you do when the human doesn't respond in time?

Genuinely want to hear how other people have solved the parts I'm still fumbling.

I have been building AI automations for clients and kept hitting the same problem ... the AI wants to do something risky (issue a refund, delete a record, update payment details) and there's no clean way to pause, get a human to review it, and let the workflow continue.

Most teams solve this with Slack messages, emails, or hardcoded approval logic scattered across their codebase.

I created Decree , a simple approval API.. Your agent or workflow sends a POST with the action it wants to take. Decree checks it against your policies (auto-approve, require human review, or block), shows it in a central inbox if a human needs to look, and fires a signed webhook back when the decision is made.

One endpoint. Policy engine. Approval inbox. Audit trail. Webhook callbacks.

It's not tied to any specific platform , anything that can make an HTTP request works. n8n, LangGraph or custom agents, whatever.

Still early. Looking for 2-3 people building AI agents or automations who are dealing with this problem. Happy to help integrate it personally in exchange for honest feedback.

Decree

A few weeks ago I asked how people were handling human approvals in production n8n workflows. The discussion was incredibly helpful (Wait nodes, webhook resumes, approval state machines, batching, idempotency, etc.).

Based on that feedback, I built a initial multi-tenant approval tool .

The goal isn't to replace n8n. It's to centralize human approval state, policies, routing, audit history, and callbacks while letting n8n continue orchestrating the workflow.

I'm looking for people already running approval workflows in production and are aware of approval processing and human in the loop patterns to provide me a feedback

If you're currently using Slack, email, forms, or custom approval logic for things like:

• customer refunds
• supplier changes
• AI-generated content
• finance approvals
• other high-impact actions

I would love to help integrate one development or testing workflow personally in exchange for honest feedback.

exploring a problem around human approvals in n8n workflows and would love to learn how others are handling it in production.

Imagine a workflow where an AI agent proposes an action such as:

- refunding a customer
- sending a quotation
- changing supplier bank details
- publishing content
- updating or deleting sensitive data.

The workflow should not execute the action immediately. It needs to pause, send the request to the correct person, wait for a decision, and then continue based on whether the action was approved, edited, rejected, or expired.

The flow might look like this:
AI or workflow proposes an action

→ evaluate business rules
→ automatically allow, block, or request approval
→ authorized person reviews the context
→ approve, edit and approve, or reject
→ send the decision back to n8n
→ continue or stop the workflow
→ preserve an audit trail

I know this can be built using Wait nodes, webhooks, forms, Slack, email, databases, and custom logic. But once requirements such as these appear, it seems to become significantly more complicated:

Eg :
- different approvers depending on amount or risk
- approval deadlines and expiry paths
- editing the proposed action before approval
- callback retries
- preventing duplicate execution
- reusing the same policies across multiple workflows
- keeping a complete audit trail.

For people running n8n in production:

1. What kinds of actions currently require human approval?
2. How are you implementing approvals today?
3. Do you use Slack, email, forms, the n8n chat interface, or a custom application?
4. Have you had to rebuild similar approval logic across multiple workflows?

I’m trying to understand whether this is a recurring production problem or something most teams and how each team solve easily inside n8n in a creative way.

A weird milestone today:

The Problem Miner API just crossed 100,000+ requests in total.

Still tiny in SaaS terms.
But honestly… seeing something I built quietly running in the background every day feels surreal.

most of the requests are agents extracting frustrations

Currently preparing for Google SWE III (L4) interviews and would really appreciate advice from people who recently passed the first coding rounds / onsite loop

Background:

• ~6 years backend experience
• Mostly Golang, distributed systems, APIs, async processing, production systems
• Stronger on practical engineering than competitive programming

So far I’ve focused mostly on:

• Arrays / HashMaps
• Two pointers
• Sliding window
• Stack basics

Right now I’m planning to heavily focus next on:

• Trees
• BFS/DFS
• Graph traversal
• Intervals
• Binary Search

Main questions:

1. If you passed Google L4 recently, what topics/patterns showed up the most?
2. Did you feel medium-level mastery mattered more than grinding hard problems?
3. Was communication + structured thinking as important as people say?
4. Would you recommend rescheduling if preparation time is short, or is interview experience itself valuable even on a first attempt?
5. Any advice specifically for backend engineers transitioning into DSA-style interviews?

Would really appreciate any honest advice or reality checks 🙂